The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index; none of the 48 sources its claims cite is checked for support yet.
Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms
Open accessAbstract
Individual subscribers can store articles and searches using a feature on the Journal's website (NEJM.org)called "My Account."Each article and search result links to this feature.Users can create personal folders and move articles into them for convenient retrieval later.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
0 of 48 cited sources checked · 0 supported · 0 flagged
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Use of a calculator that lowers the estimate of VBAC success for people of color could exacerbate these disparities.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: Nonwhite women in the United States have higher cesarean-section rates than white women.
- Rests on an assumption: Successful vaginal deliveries have health benefits, including fewer surgical complications, faster recovery, and fewer complications in later pregnancies.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When used preoperatively to assess risk, these calculations could steer minority patients, deemed to be at higher risk, away from surgery.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“The American Heart Association (AHA) Get with the Guidelines-Heart Failure Risk Score predicts the risk of death in patients admitted to the hospital.9”
What the sources say
- Not checked yetcites [9] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Most race corrections implicitly, if not explicitly, operate on the assumption that genetic difference tracks reliably with race.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearNone of the sources cited for this claim have been checked yet.
In the paper
“Studies of the genetic structure of human populations continue to find more variation within racial groups than between them.34,35”
What the sources say
- Not checked yetcites [34] source
- Not checked yetcites [35]
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“The developers of the KDRI do not provide possible explanations for this difference.12”
What the sources say
- Not checked yetcites [12] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Meanwhile, black patients in the United States still have longer wait times for kidney transplants than nonblack patients.29”
What the sources say
- Not checked yetcites [29] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“The AHA does not provide a rationale for this adjustment.”
What the sources say
- Not checked yetcites [9] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“The race adjustment is based on an empirical finding that black donors' kidneys perform worse than nonblack donors' kidneys, regardless of the recipient's race.28”
What the sources say
- Not checked yetcites [28] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Instead, if adjustments deter clinicians from offering clinical services to certain patients, they risk baking inequity into the system.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- Probably not a substantial claim. 23
- It may state two separate claims as one. 17
None of the sources cited for this claim have been checked yet.
In the paper
“Explanations that have been given for this finding include the notion that black people release more creatinine into their blood at baseline, in part because they are reportedly more muscular.11,25”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“It assigns three additional points to any patient identified as “nonblack,” thereby categorizing all black patients as being at lower risk.”
What the sources say
- Not checked yetcites [9] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Epidemiologists continue to debate how to responsibly make causal inferences based on race.33”
What the sources say
- Not checked yetcites [33] source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, despite mounting evidence that race is not a reliable proxy for genetic difference, the belief that it is has become embedded, sometimes insidiously, within medical practice.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt may state two separate claims as one.
In the paper
“Clinicians are advised to use this risk score to guide decisions about referral to cardiology and allocation of health care resources.”
What the sources say
- Not checked yetcites [9] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The Vaginal Birth after Cesarean (VBAC) algorithm predicts the risk posed by a trial of labor for someone who has previously undergone cesarean section.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Such situations, however, are exceedingly unlikely.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“while black people already have higher rates of end-stage kidney disease and death due to kidney failure than the overall population.25”
What the sources say
- Not checked yetcites [25] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Our understanding of race and human genetics has advanced considerably since 2003, yet these insights have not led to clear guidelines on the use of race in medicine.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Physicians use these algorithms to individualize risk assessment and guide clinical decisions.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Analyses have cast doubt on this claim, 26 but the “race-corrected” eGFR remains the standard.”
What the sources say
- Not checked yetcites [26] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Some algorithm developers offer no explanation of why racial or ethnic differences might exist.Others offer rationales, but when these are traced to their origins, they lead to outdated, suspect racial science or to biased data.22,30,31”
What the sources say
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“troubling because black people already have higher rates of maternal mortality.30”
What the sources say
- Not checked yetcites [30] source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given this complexity, it is insufficient to translate a data signal into a race adjustment without determining what race might represent in the particular context.”
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“They examined data sets of clinical outcomes and patient characteristics and then performed regression analyses to identify which patient factors correlated significantly with the relevant outcomes.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Researchers then decided that it was appropriate -even essential -to adjust for race in their model.”
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
In the paper
“The Society of Thoracic Surgeons produces elaborate calculators to estimate the risk of death and other complications during surgery.10”
What the sources say
- Not checked yetcites [10] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Use of the KDRI may do just that.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Changing to any other race or ethnicity does not increase the estimated risk of death as compared with a white patient, but it does change the risk of renal failure, stroke, or prolonged ventilation.”
What the sources say
- Not checked yetcites [10] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the empirical differences seen between racial groups were actually due to genetic differences, then race adjustment might be justified: different coefficients for different bodies.”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Since it is cumbersome to measure kidney function directly, researchers have developed equations that determine the estimated glomerular filtration rate (eGFR) from an accessible measure, the serum creatinine level.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The calculators include race and ethnicity because of observed differences in surgical outcomes among racial and ethnic groups; the authors acknowledge that the mechanism underlying these differences is not known.”
What the sources say
- Not checked yetcites [10] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Conversely, race adjustments that yield higher estimates of kidney function in black patients might delay their referral for specialist care or transplantation and lead to worse outcomes”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Since the health care system has spent more money, on average, on white patients than on black patients, the tool returned higher risk scores for white patients than for black patients.”
What the sources say
- Not checked yetcites [36] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This decision reflects a default assumption in medicine: it is acceptable to use race adjustment even without understanding what race represents in a given context.”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“Some tools, including eGFR and the VBAC calculator, have already been challenged; clinicians have advocated successfully for their institutions to remove the adjustment for race.43,44”
What the sources say
- Not checked yetcites [43]
- Not checked yetcites [44]
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, when clinicians insert race into their tools, they risk interpreting racial disparities as immutable facts rather than as injustices that require intervention.”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The criminal justice system, for instance, uses recidivism-prediction tools to guide decisions about bond amounts and prison sentences.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Clinicians and medical researchers typically use the categories recommended by the Office of Management and Budget: five races and two ethnicities.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Guidelines are silent on such issues -an indication of their inadequacy.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“But if the underlying data reflect racist social structures, then their use in predictive tools cements racism into practice and policy.”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When these tools influence high-stakes decisions, whether in the clinic or the courtroom, they propagate inequity into our future.”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
In the paper
“The Society of Thoracic Surgeons acknowledged concerns raised by clinicians and policymakers “that inclusion of SES factors in risk models may 'adjust away' disparities in quality of care.”Nonetheless, it proceeded to consider “all preoperative factors that are independently and significantly associated with outcomes””
What the sources say
- Not checked yetcites [10] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Our understanding of race has advanced considerably in the past two decades.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“One tool, COMPAS (Correctional Offender Management Profiling for Alternative Sanctions), while not using race per se, uses many factors that correlate with race and returns higher risk scores for black defendants.40”
What the sources say
- Not checked yetcites [40]
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A second problem arises from the ways in which racial and ethnic categories are operationalized.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These scores may well have led to more referrals for white patients to specialty services, perpetuating both spending discrepancies and race bias in health care.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper relies on this without support.
Nonwhite women in the United States have higher cesarean-section rates than white women.
“Nonwhite U.S. women continue to have higher rates of cesarean section than white U.S. women.”
The paper relies on this without support.
Successful vaginal deliveries have health benefits, including fewer surgical complications, faster recovery, and fewer complications in later pregnancies.
“The health benefits of successful vaginal deliveries are well known, including lower rates of surgical complications, faster recovery time, and fewer complications during subsequent pregnancies.”
The paper relies on this without support.
Minority patients routinely have different health outcomes from white patients.
“Since minority patients routinely have different health outcomes from white patients, race and ethnicity often correlated with the outcome of interest.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Such “corrections” are used in a variety of areas of medicine, from endocrinology and nephrology to oncology and obstetrics, which often have the effect of requiring patients of color to be sicker before they qualify for the same level of care as a White patient.24–26”
From Challenging Race-Based Medicine Through Historical Education About the Social Construction of Race (2023) · cites this paper as [26]
Experimental — accuracy not yet measuredIn the citing paper
“If a classifier deployed in a clinical pipeline mistakenly underdiagnosed a certain subgroup (for example, Black patients) more than others due to the lower prevalence of the disease, this still leads to disadvantage for members of that group and could lead to serious ethical concerns 8 .”
From Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations (2021) · cites this paper as [8]
In the citing paper
“The choice of these subgroups is motivated by the clear history, in both traditional medicine and AI algorithms, of bias for subgroups on these axes 6,8,10,11 .”
From Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations (2021) · cites this paper as [8]
In the citing paper
“We note that some of our observed differences in underdiagnosis have been established in other areas in clinical care, such as underdiagnosis of female patients 9,10 , Black patients 5,8,9 and patients with a low socioeconomic status 36 .”
From Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations (2021) · cites this paper as [8]
In the citing paper
“However, algorithmic bias has harmed minoritized communities in housing, banking, and education, and health care is no different.1”
From Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care (2023) · cites this paper as [1]
In the citing paper
“In reality, however, AI models are frequently found to be biased toward certain patient groups, leading to disparities not only in performance but also in potential or actual clinical benefits [7–9].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [9]
In the citing paper
“In addition to racial and ethnic imbalances in training data, there has been increased scrutiny on the use of race and ethnicity in predictive clinical algorithms [9].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [9]
In the citing paper
“Indeed, there is mounting evidence that race is not a reliable proxy for genetic differences and, despite this lack of evidence, it has become acceptable to adjust for race even without understanding what it represents in a given context [9].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [9]
In the citing paper
“Indeed, there is mounting evidence that race is not a reliable proxy for genetic differences and, despite this lack of evidence, it has become acceptable to adjust for race even without understanding what it represents in a given context [9].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [9]
In the citing paper
“Race and ethnicity are social constructs, not biological constructs [9,90].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [9]
In the citing paper
“Unethically and irresponsibly designed AI/ML solutions deployed in the healthcare setting can exacerbate and perpetuate systematic biases and disparities for those from marginalized groups [27–29].”
From ChatGPT and large language models in academia: opportunities and challenges (2023) · cites this paper as [27]
Citations
Cites
A New Equation to Estimate Glomerular Filtration Rate
Levey, A. S., Stevens, L. A., Schmid, C. H., Zhang, Y. (., Castro, A. F., Feldman, H. I., Kusek, J. W., Eggers, P., Van Lente, F., Greene, T., et al. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-150-9-200905050-00006 · Cited by 25,603
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137
Race after technology. Abolitionist tools for the new Jim Code
Merz, S. (2020) · Ethnic and Racial Studies · doi:10.1080/01419870.2020.1715454 · Cited by 1,889
The Importance of Race and Ethnic Background in Biomedical Research and Clinical Practice
Burchard, E. G., Ziv, E., Coyle, N. E., Gomez, S. L., Tang, H., Karter, A. J., Mountain, J. L., Pérez‐Stable, E. J., Sheppard, D., & RISCH, N. J. (2003) · New England Journal of Medicine · doi:10.1056/nejmsb025007 · Cited by 1,104
A Comprehensive Risk Quantification Score for Deceased Donor Kidneys: The Kidney Donor Risk Index
Rao, P. S., Schaubel, D. E., Guidinger, M. K., Andreoni, K. A., Wolfe, R. A., Merion, R. M., Port, F. K., & Sung, R. S. (2009) · Transplantation · doi:10.1097/tp.0b013e3181ac620b · Cited by 1,079
Fatal invention: how science, politics, and big business re-create race in the twenty-first century
Roberts, D. E. (2011) · Choice Reviews Online · doi:10.5860/choice.49-2029 · Cited by 1,028
Methods for the Scientific Study of Discrimination and Health: An Ecosocial Approach
Krieger, N. (2012) · American Journal of Public Health · doi:10.2105/ajph.2011.300544 · Cited by 854
Taking race out of human genetics
Yudell, M., Roberts, D., DeSalle, R., & Tishkoff, S. (2016) · Science · doi:10.1126/science.aac4951 · Cited by 631
Cooper, R., Kaufman, J. S., & Ward, R. H. (2003) · New England Journal of Medicine · doi:10.1056/nejmsb022863 · Cited by 628
Use of race and ethnicity in biomedical publication
Kaplan, J. B. (2003) · JAMA · doi:10.1001/jama.289.20.2709 · Cited by 609
Flagged first, then by citations
Research works printed but not linked: 2
- Jones DS, Dorr GM, Pollock A. Symposium: race, pharmaceuticals, and medical technology. J Law Med Ethics 2008;36:439-545.
- Kanis JA. Assessment of osteoporosis at the primary health care level. WHO Scientific Group technical report. Sheffield, United Kingdom: World Health Organization Collaborating Centre for Metabolic Bone Diseases, University of Sheffield, 2007.
Cited by
Sorted by citations
Items 701–800 of 1,003
Grantz, K. L., Lee, W., Mack, L. M., Cortés, M. S., Gonçalves, L. F., Espinoza, J., Newman, R., Grobman, W. A., Wapner, R. J., Fuchs, K. M., et al. (2024) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2024.05.049 · Cited by 9
Balancing Act: Evaluating People’s Perceptions of Fair Ranking Metrics
Alkhathlan, M., Cachel, K., Shrestha, H., Harrison, L., & Rundensteiner, E. A. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3659018 · Cited by 9
Gangopadhyaya, A., Dubay, L. C., Johnston, E. M., & Pancini, V. (2024) · Health Affairs Scholar · doi:10.1093/haschl/qxae092 · Cited by 10
A Clinician’s Guide to Understanding Bias in Critical Clinical Prediction Models
de Matos, J. C. R. G., Gallifant, J., Chowdhury, A., Economou-Zavlanos, N., Charpignon, M., Gichoya, J. W., Celi, L. A., Nazer, L. H., King, H., & Wong, A.-K. I. (2024) · Critical Care Clinics · doi:10.1016/j.ccc.2024.05.011 · Cited by 11
Żuradzki, T., & Malinowska, J. (2024) · Synthese · doi:10.1007/s11229-024-04769-8 · Cited by 7
Foryciarz, A., Gladish, N., Rehkopf, D. H., & Rose, S. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf009 · Cited by 8
Against reflexive recalibration: towards a causal framework for addressing miscalibration
Swaminathan, A., Srivastava, U., Tu, L., López, I., Shah, N. H., & Vickers, A. J. (2025) · Diagnostic and Prognostic Research · doi:10.1186/s41512-024-00184-2 · Cited by 9
Khan, S. S., Abdalla, M., Bello, N. A., Blyler, C. A., Carter, J., Commodore-Mensah, Y. Y., Ferdinand, K. C., Johnson, H. M., Jones, D. W., Khera, A., et al. (2025) · Circulation · doi:10.1161/cir.0000000000001355 · Cited by 13
Self-citationA Comprehensive Review of Bias in AI, ML, and DL Models: Methods, Impacts, and Future Directions
Kumar, A., Dhanka, S., Sharma, A., Sharma, A., Nain, M., Kumar, P., Gupta, A. R., Bansal, J., Saxena, N. K., & Pant, R. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10483-6 · Cited by 11
Malhamé, I., Raker, C., Hardy, E. J., Spalding, H., Bouvier, B. A., Hurlburt, H., Vrees, R., Daskalopoulou, S. S., Nerenberg, K. A., Savitz, D. A., et al. (2022) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2022.05.007 · Cited by 8
Su, Y., Buist, D. S. M., Lee, J. M., Ichikawa, L., Miglioretti, D. L., Bowles, E. J. A., Wernli, K. J., Kerlikowske, K. M., Tosteson, A. N. A., Lowry, K. P., et al. (2023) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-22-0677 · Cited by 9
Stites, S. D., & Coe, N. B. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-220507 · Cited by 10
From ‘if‐then’ to ‘what if?’ Rethinking healthcare algorithmics with posthuman speculative ethics
Smith, J., Klumbytė, G., & Britton, R. L. (2023) · Nursing Philosophy · doi:10.1111/nup.12447 · Cited by 9
Mulinari, S., & Bredström, A. (2023) · Sociology of Health & Illness · doi:10.1111/1467-9566.13709 · Cited by 10
Tammemägi, M. C., Cina, K., Kitts, A. B., Koop, D., Petereit, M. A., Sargent, M., & Petereit, D. G. (2023) · Cancer · doi:10.1002/cncr.34947 · Cited by 9
Development of a prediction model of postpartum hospital use using an equity-focused approach
Janevic, T. M., Tomalin, L. E., Glazer, K. B., Boychuk, N., Kern‐Goldberger, A. R., Burdick, M., Howell, F., Suárez‐Fariñas, M., Egorova, N., Zeitlin, J. A., et al. (2023) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2023.10.033 · Cited by 9
Chen, R. Y.-W., & Shi, J. (2023) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2023.110686 · Cited by 9
Race Correction and Algorithmic Bias in Atrial Fibrillation Wearable Technologies
Merid, B., & Volpe, V. V. (2023) · Health Equity · doi:10.1089/heq.2023.0034 · Cited by 9
Trainees’ Perspectives on the Next Era of Assessment and Precision Education
Marcotte, K. M., Manriquez, J. A. N., Hunt, M., Spadafore, M., Perrone, K. H., & Zhou, C. Y. (2023) · Academic Medicine · doi:10.1097/acm.0000000000005602 · Cited by 9
Physician Workforce Diversity Is Still Necessary and Achievable if It Is Intentionally Prioritized
Vereen, R. J., & Wolf, M. F. (2024) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-024-01953-x · Cited by 9
Antimicrobial Dosing in Specific Populations and Novel Clinical Methodologies: Kidney Function
Pai, M. P. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2179 · Cited by 9
Reid, A., Brandes, R., Butler-MacKay, D., Ortiz, A., Kramer, S., Sivashanker, K., & Mate, K. S. (2021) · NEJM Catalyst · doi:10.1056/cat.21.0320 · Cited by 10
Improving polygenic prediction with genetically inferred ancestry
Naret, O., Kutalik, Z., Hodel, F., Xu, Z. M., Marques-Vidal, P. M., & Fellay, J. (2022) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2022.100109 · Cited by 9
Golder, S., O’Connor, K., Wang, Y., Stevens, R. C., & Gonzalez‐Hernandez, G. (2022) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-122120-025806 · Cited by 11
Sampson, M. L., Wolska, A. T., Amar, M. J., Ueda, M., Dunbar, R. L., Soffer, D. E., & Remaley, A. T. (2022) · Clinical Chemistry · doi:10.1093/clinchem/hvac120 · Cited by 9
Short Report: Race and Ethnicity Misclassification in Kidney Transplantation Research
Kernodle, A., Thompson, V. L., Chen, X., Norman, S. P., Segev, D. L., Purnell, T. S., & McAdams‐DeMarco, M. (2022) · Transplantation Direct · doi:10.1097/txd.0000000000001373 · Cited by 9
Stelter, M., & Schweinberger, S. R. (2022) · British Journal of Psychology · doi:10.1111/bjop.12615 · Cited by 9
Castelijns, M. C., Hageman, S. H. J., Teraa, M., van der Meer, M. G., Westerink, J., Costa, F., ten Berg, J. M., & Visseren, F. L. J. (2023) · American Heart Journal · doi:10.1016/j.ahj.2023.02.011 · Cited by 9
Understanding Race-Based Medicine and Its Impact on Radiology
Medrano, D., Matsumoto, M. M., Flores, E. J., & Dako, F. (2023) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2023.03.017 · Cited by 10
Chasing Your Long Tails: Differentially Private Prediction in Health Care SettingsPreprint
Suriyakumar, V. M., Papernot, N., Goldenberg, A., & Ghassemi, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06667 · Cited by 9
Fetters, M. D., Wu, J. P., & Chandanabhumma, P. P. (2020) · Journal of Mixed Methods Research · doi:10.1177/1558689820977233 · Cited by 9
Dynamic Errors in Pulse Oximetry Preclude Use of Correction Factor
Fawzy, A., Valbuena, V. S. M., Chesley, C. F., Wu, T. D., & Iwashyna, T. J. (2022) · Annals of the American Thoracic Society · doi:10.1513/annalsats.202210-872le · Cited by 9
Clinical decision-making and algorithmic inequality
Challen, R., & Danon, L. (2023) · BMJ Quality & Safety · doi:10.1136/bmjqs-2022-015874 · Cited by 9
Equity and AI Governance at Academic Medical Centers
(2024) · The American Journal of Managed Care · doi:10.37765/ajmc.2024.89555 · Cited by 8
Harrison, T. N., Chen, Q., Lee, M. Y., Munis, M. A., Morrissette, K. L., Sundar, S. V., Pareja, K., Nourbakhsh, A., Shu, Y., Willey, C. J., et al. (2022) · Kidney Medicine · doi:10.1016/j.xkme.2022.100577 · Cited by 8
Comparison of Race-Neutral versus Race-Specific Spirometry Equations for Evaluation of Child Asthma
Non, A. L., Li, X., Jones, M. R., Oken, E., Hartert, T. V., Schoettler, N., Gold, D. R., Ramratnam, S. K., Schauberger, E. M., Tantisira, K. G., et al. (2024) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202407-1288oc · Cited by 8
Faridi, K. F., Wang, Y., Minges, K. E., Smilowitz, N. R., McNamara, R. L., Kontos, M. C., Wang, T. Y., Connors, A. C., Clary, J. M., Osborne, A. D., et al. (2025) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.124.011259 · Cited by 8
Grobman, B., Turkson‐Ocran, R., Zhang, M., & Juraschek, S. P. (2025) · Kidney Medicine · doi:10.1016/j.xkme.2025.101045 · Cited by 8
NHS must tackle racism and sexism for the benefit of patients, staff, and society
Bamrah, J., Womersley, K., Nagpaul, C., Coghill, Y., Issa, R., & Hazard, L. (2025) · BMJ · doi:10.1136/bmj.r1334 · Cited by 8
Panagides, R., & Keim‐Malpass, J. (2025) · International Journal of Nursing Studies Advances · doi:10.1016/j.ijnsa.2025.100380 · Cited by 10
Artificial Intelligence and Health Equity
Bright, T. J., & Norris, K. C. (2025) · Annual Review of Medicine · doi:10.1146/annurev-med-043024-125309 · Cited by 8
Educational intervention against biological racism
Degife, E., Ijeli, C., Muhammad, M. I., Nobles, A., & Reisman, A. B. (2021) · The Clinical Teacher · doi:10.1111/tct.13403 · Cited by 8
Things We Do for No Reason™: Routine inclusion of race in the history of present illness
Gau, J., Nwora, C., Eldakar-Hein, S. T., Goel, M. S., Lahey, T., & Repp, A. B. (2021) · Journal of Hospital Medicine · doi:10.12788/jhm.3650 · Cited by 8
Designing Actionable Solutions and Curriculum for Pain Disparities Education
Adams, M. C. B., Denizard-Thompson, N. M., DiGiacobbe, G., Williams, B. L., & Brooks, A. K. (2021) · Pain Medicine · doi:10.1093/pm/pnab289 · Cited by 8
Truong, M., Lazarus, M. D., Ochoa, G. G., & Brand, G. (2022) · Nurse Education Today · doi:10.1016/j.nedt.2022.105458 · Cited by 8
Racial Fairness in Precision Medicine: Pediatric Asthma Prediction Algorithms
Pennington, J., Rasnick, E., Martin, L. J., Biagini, J. M., Baye, T. M., Parsons, A. A., Hershey, G. K. K., Ryan, P., & Brokamp, C. (2022) · American Journal of Health Promotion · doi:10.1177/08901171221121639 · Cited by 8
(Some) algorithmic bias as institutional bias
Flowerman, C. H. (2023) · Ethics and Information Technology · doi:10.1007/s10676-023-09698-7 · Cited by 8
Regan, E. A., Lowe, M. E., Make, B. J., Curtis, J. L., Chen, Q., Cho, M. H., Crooks, J., Lowe, K., Wilson, C. G., O'Brien, J., et al. (2023) · Journal of General Internal Medicine · doi:10.1007/s11606-023-08185-5 · Cited by 8
Jarrar, F., Tennankore, K., & Vinson, A. J. (2023) · American Journal of Transplantation · doi:10.1016/j.ajt.2023.04.025 · Cited by 8
Poteat, T., Lett, E., Rich, A. J., Jiang, H., Wirtz, A. L., Radix, A., Reisner, S. L., Harris, A. B., Malone, J., Cava, W. L., et al. (2023) · Health Equity · doi:10.1089/heq.2023.0066 · Cited by 10
Frost, E. D., Penckofer, M., Zhang, L., Sprankle, K. W., Vigilante, N. F., Elgendy, O., Ackerman, J., Kalladanthyil, A., Koneru, M., Yi, Z., et al. (2024) · Stroke Vascular and Interventional Neurology · doi:10.1161/svin.123.001306 · Cited by 8
Current perspectives on the use of artificial intelligence in critical patient safety
Barea‐Mendoza, J. A., Fernández, M. V., Pardo, À., & Álvarez, J. G. (2024) · Medicina Intensiva (English Edition) · doi:10.1016/j.medine.2024.04.002 · Cited by 10
The impact of devaluing Women of Color: stress, reproduction, and justice
Mayne, G., & Ghidei, L. A. (2024) · Birth · doi:10.1111/birt.12825 · Cited by 8
Clinicians’ perspectives on race-specific guidelines for hypertensive treatment
Rabay, C. J., López, C., Streuli, S. A., Mayes, E., Rajagopalan, R. M., & Non, A. L. (2024) · Social Science & Medicine · doi:10.1016/j.socscimed.2024.116938 · Cited by 8
Goldstein, B. A., Mohottige, D., Bessias, S., & Cary, M. P. (2024) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2024.04.008 · Cited by 8
Tejera, C. H., Ware, E. B., Hicken, M. T., Kobayashi, L. C., Wang, H., Adkins‐Jackson, P. A., Blostein, F., Zawistowski, M., Mukherjee, B., & Bakulski, K. M. (2024) · Communications Medicine · doi:10.1038/s43856-024-00569-w · Cited by 11
Vyas, D. A., Zhao, S. J., Lai, P. S., Rodriguez‐Lopez, J., Schmidt, E. P., Brown, R. H., Hibbert, K. A., Hardin, C. C., & North, C. M. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.7304 · Cited by 8
Self-citationRacial/ethnic differences in maternal resilience and associations with low birthweight
Montoya‐Williams, D., Passarella, M. A., Grobman, W. A., & Lorch, S. A. (2020) · Journal of Perinatology · doi:10.1038/s41372-020-00837-2 · Cited by 8
Johnson, W. G. (2020) · The Journal of Law Medicine & Ethics · doi:10.1177/1073110520979374 · Cited by 9
The Clinician's Guide to the Machine Learning Galaxy
Shen, L., Kann, B. H., Taylor, R. A., & Shung, D. L. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.658583 · Cited by 8
Tso, J., Turner, C. G., Liu, C., Galante, A., Gilson, C., Clark, C. B., Taylor, H. A., Quyyumi, A. A., Baggish, A. L., & Kim, J. H. (2021) · British Journal of Sports Medicine · doi:10.1136/bjsports-2021-104333 · Cited by 8
Marijuana use, fetal growth, and uterine artery Dopplers
Bruno, A. M., Blue, N. R., Allshouse, A. A., Haas, D. M., Shanks, A. L., Grobman, W. A., Simhan, H. N., Reddy, U. M., Silver, R. M., & Metz, T. D. (2021) · The Journal of Maternal-Fetal & Neonatal Medicine · doi:10.1080/14767058.2021.1960973 · Cited by 8
Time to end the misuse of race in medicine: cases from nephrology
Gopalakrishnan, C., & Patorno, E. (2021) · BMJ · doi:10.1136/bmj.n2435 · Cited by 8
Examining Whiteness in Obstetric and Pediatric Simulations: A Content Analysis
Lamberson, J., Pearson, P., Crooks, N. K., & Ricca, P. (2021) · Journal of Nursing Education · doi:10.3928/01484834-20211102-01 · Cited by 8
Pooled Cohort Probability Score for Subclinical Airflow Obstruction
Bhatt, S. P., Balte, P. P., Schwartz, J. E., Jaeger, B. C., Cassano, P. A., Chaves, P. H. M., Couper, D. J., Jacobs, D. R., Kalhan, R., Kaplan, R. C., et al. (2022) · Annals of the American Thoracic Society · doi:10.1513/annalsats.202109-1020oc · Cited by 8
Immune mechanisms behind prostate cancer in men of African ancestry: A review
Sentana‐Lledo, D., Sartor, O. A., Balk, S. P., & Einstein, D. J. (2022) · The Prostate · doi:10.1002/pros.24333 · Cited by 9
Racism, Not Race: A Physician Perspective on Anti‐Black Racism in America
Clayborne, E. P. (2022) · The Hastings Center Report · doi:10.1002/hast.1365 · Cited by 8
A novel approach to joint prediction of preeclampsia and delivery timing using semicompeting risks
Reeder, H. T., Haneuse, S., Modest, A. M., Hacker, M. R., Sudhof, L. S., & Papatheodorou, S. (2022) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2022.08.045 · Cited by 7
Communicating and Using Dementia Risk Evidence
Rosen, A. C. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220722 · Cited by 9
Collaro, A., Chang, A. B., Marchant, J. M., Vicendese, D., Chatfield, M. D., Cole, J. F., Blake, T. L., & McElrea, M. S. (2022) · CHEST Journal · doi:10.1016/j.chest.2022.10.014 · Cited by 8
Overt and Occult Hypoxemia in Patients Hospitalized With COVID-19
Gadrey, S. M., Mohanty, P., Haughey, S. P., Jacobsen, B. A., Dubester, K. J., Webb, K. M., Kowalski, R. L., Dreicer, J. J., Andris, R. T., Clark, M., et al. (2023) · Critical Care Explorations · doi:10.1097/cce.0000000000000825 · Cited by 8
Hidden in plain sight in the delivery room – the Apgar score is biased
Grunebaum, A. N., Bornstein, E., Dudenhausen, J. W., Lenchner, E., Jones, M. D. F., Varrey, A., Lewis, D., & Chervenak, F. A. (2023) · Journal of Perinatal Medicine · doi:10.1515/jpm-2022-0550 · Cited by 10
Kim, J. H., Kang, M., Kang, E., Ryu, H., Jeong, Y., Kim, J., Park, S. K., Jeong, J. C., Yoo, T., Kim, Y., et al. (2023) · Kidney Research and Clinical Practice · doi:10.23876/j.krcp.22.206 · Cited by 8
Lin, M., Xiao, Y., Hou, B., Wanyan, T., Sharma, M. M., Wang, Z., Wang, F., Tassel, S. H. V., & Peng, Y. (2023) · PubMed Central · Cited by 11
Bentz, M., Saperstein, A., Fullerton, S. M., Shim, J. K., & Lee, S. S. (2023) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2023.100243 · Cited by 9
Reconciling the past and considering the future of pulmonary function test interpretation
Dinh‐Xuan, A. T., Graham, B. L., Thompson, B., Miller, M. R., & Stanojevic, S. (2024) · European Respiratory Journal · doi:10.1183/13993003.02225-2023 · Cited by 8
A Dynamic Bayesian Model for Identifying High-Mortality Risk in Hospitalized COVID-19 Patients
Boroujeni, A. M., Mendoza, R., Stopard, I. J., Lambert, B. C., & Zuretti, A. R. (2021) · Infectious Disease Reports · doi:10.3390/idr13010027 · Cited by 8
Murugappan, G., Alvero, R. J., Lyell, D. J., Khandelwal, A., & Leonard, S. A. (2021) · Fertility and Sterility · doi:10.1016/j.fertnstert.2021.06.024 · Cited by 8
Kim, J. P. (2021) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2021.02.021 · Cited by 8
Advancements in MELD Score and Its Impact on Hepatology
Hudson, D., Cortez, F. J. V., de León, I. H. D., Malhi, G., Rivas, A., Afzaal, T., Rad, M. R., Díaz, L. A., Khan, M. Q., & Arab, J. P. (2024) · Seminars in Liver Disease · doi:10.1055/a-2464-9543 · Cited by 8
A procedural overview of why, when and how to use machine learning for psychiatry
Lucasius, C., Ali, M., Patel, T., Kundur, D., Szatmári, P., Strauss, J., & Battaglia, M. (2025) · Nature Mental Health · doi:10.1038/s44220-024-00367-2 · Cited by 7
Jayasekera, J. C., Wilson, O. W. A., Wojcik, K. M., Kerr, E. M., Brick, R. S., Berrigan, D., Sheng, J. Y.-S., Fujii, T., Thomas, K. S., Parson, H. K., et al. (2025) · Journal of Cancer Survivorship · doi:10.1007/s11764-025-01750-3 · Cited by 7
Gupta, R., Sasaki, M., Taylor, S. L., Fan, S., Hoch, J. S., Zhang, Y., Crase, M., Tancredi, D. J., Adams, J. Y., & Ton, H. (2025) · Journal of General Internal Medicine · doi:10.1007/s11606-025-09462-1 · Cited by 13
Racial and Ethnic Disparities in Cardiotoxicity in Patients With Cancer Treated With Anthracyclines
Zhang, L.-L., Song, J., Hanif, W., Clark, R., Haroun, M., McNaughton, C., Slipczuk, L., Garcia, M. J., Pu, M., Gongora, C. A., et al. (2025) · Journal of the American Heart Association : Cardiovascular and Cerebrovascular Disease · doi:10.1161/jaha.124.037780 · Cited by 7
The Emergence of Applied Artificial Intelligence in the Realm of Value Based Musculoskeletal Care
Hunter, J., Dentino, P., & Jayakumar, P. (2025) · Current Reviews in Musculoskeletal Medicine · doi:10.1007/s12178-025-09982-7 · Cited by 9
Zhou, Y. F., Nie, H., Gong, X., Dai, M., Guo, Z., Deng, X., Li, M., Liu, Y., Sun, L., Tang, X., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01835-4 · Cited by 9
Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) Equations: What Clinicians Need to Know?
Abdul Jabbar, A. B., Inam, M., Butt, N., Khan, S. S., Sheikh, S., Khoja, A., Perry, B., Gomez, G. Z., Slipczuk, L., & Virani, S. S. (2025) · Current Atherosclerosis Reports · doi:10.1007/s11883-025-01320-z · Cited by 7
Ethical Issues in Organ Transplantation: A Position Paper From the American College of Physicians
Esbensen, K. L., DeCamp, M. W., Crigger, E. J., Sulmasy, L. S., Esbensen, K. L., Carney, J. K., Moreno, A., Arbaje, A. I., Bruera, E., Collamer, A. N., et al. (2025) · Annals of Internal Medicine · doi:10.7326/annals-25-01738 · Cited by 7
Gold, J. I., Elkaim, Y., Gold, N. B., Asher, S. B., Raper, A. C., Condit, C., Bogus, Z. C., Elysee, I., Hennessy, L., Kennedy, E., et al. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.11.010 · Cited by 7
Racial Differences in Urine Testing of Febrile Young Children Presenting to Pediatric Hospitals
Ramgopal, S., Tidwell, N., Shaikh, N., Shope, T. R., & Macy, M. L. (2021) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-021-01182-6 · Cited by 7
The Apportionment of Pharmacogenomic Variation: Race, Ethnicity, and Adverse Drug Reactions
Jordan, I. K., Sharma, S., Nagar, S. D., & Mariño‐Ramírez, L. (2022) · Medical Research Archives · doi:10.18103/mra.v10i9.2986 · Cited by 9
Change Management Strategies Toward Dismantling Race-Based Structural Barriers in Radiology
Abraham, P., Hsieh, L. J., Buckner, E., Flores, E. J., Davis, M. A., Tamayo-Murillo, D., & Newton, I. G. (2023) · Academic Radiology · doi:10.1016/j.acra.2023.01.035 · Cited by 10
Ahmad, M. A., & Eckert, C. (2023) · Surgical Clinics of North America · doi:10.1016/j.suc.2023.03.002 · Cited by 8
Implementing an Automated Prediction Model to Improve Prescribing of HIV Preexposure Prophylaxis
Krakower, D., Lieberman, M. I., Marino, M., Hwang, J. H., Mayer, K. H., & Marcus, J. L. (2023) · NEJM Catalyst · doi:10.1056/cat.23.0215 · Cited by 7
Beaudoin, J., Curran, J. N., & Alexander, G. C. (2024) · AJPM Focus · doi:10.1016/j.focus.2024.100200 · Cited by 7
Apolitical Linguistics Doesn’t Exist, and It Shouldn’t
Clemons, A. M. (2024) · Oxford University Press eBooks · doi:10.1093/oso/9780197755259.003.0006 · Cited by 8
An antiracism framework for educating nursing professionals
Davis, S. L., Martin-Holland, J., Gemeda, M., & Mitchell, D. A. (2024) · Nursing Outlook · doi:10.1016/j.outlook.2024.102242 · Cited by 9
Lin, K., Granka, J. M., Shastri, A. J., Bonham, V. L., & Naik, R. P. P. (2024) · Blood Advances · doi:10.1182/bloodadvances.2024014252 · Cited by 7
Ibe-Lamberts, K. D., Ajibewa, T. A., Onyeise, D., McNeil-Young, V. A., Nmezi, N. A., & Williams, J. (2025) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-025-02394-w · Cited by 8
Anaemia, ethnicity, and cancer incidence: a retrospective cohort study in primary care
Down, L., Barlow, M., Bailey, S. E. R., Mounce, L. T. A., Merriel, S. W., Watson, J., Chen, G., & Martins, T. (2025) · British Journal of General Practice · doi:10.3399/bjgp.2024.0762 · Cited by 7
Metadata
- Authors
- Vyas, D. A., Eisenstein, L. G., & Jones, D. S.
- Affiliations
- Harvard University (US)
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2020-08-27
- Volume
- 383
- Issue
- 9
- Pages
- 874-882
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W3035885149
- Semantic Scholar
- 07a84399ee2befea66e7ed0560fc32f0e4e8a070
- Cited by
- 1,731
- References
- 35
- Citations by year
- 2026: 191 · 2025: 262 · 2024: 295 · 2023: 337 · 2022: 295 · 2021: 267 · 2020: 78
- FWCI
- 144.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Anthropology › Race, Genetics, and Society
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- clinical algorithms, racial bias, algorithmic fairness, health disparities, algorithmic discrimination
- MeSH terms
- Algorithms, Cardiology, Female, Humans, Male, Nephrology, Obstetrics, Urology, Risk Assessment, Racial Groups, Healthcare Disparities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-04, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-04 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
