Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial

May combine two claims.

The regulatory approval of the first diagnostic autonomous AI system for diabetic eye exams was based on a pivotal trial against a prognostic standard, i.e., patient outcome, showing its safety, efficacy, and lack of racial and ethnic bias for diagnosing DED in adults with diabetes with 87% sensitivity and 91% specificity.

“The regulatory approval of the first diagnostic autonomous AI system for diabetic eye exams was based on a pivotal trial against a prognostic standard, i.e., patient outcome24, showing its safety, efficacy, and lack of racial and ethnic bias for diagnosing DED in adults with diabetes with 87% sensitivity and 91% specificity24.”

• cites [24] Abramoff, M. D., Lavin, P. T., Birch, M., Shah, N. & Folk, J. C. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit. Med. 1, 39 (2018). sourceDisputed

  Split into 2 atoms — 0 confirmed, 1 partially supported, 1 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  Checked by a reasoning model — may be wrong.

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing).

Some participants in this study were familiar with autonomous AI diabetic eye exams from a prior study and may have been more accepting of participation in the current study.

“Some of the participants in this study were familiar with autonomous AI diabetic eye exams from our prior study and may have been more accepting of participation in the current study30.”

• cites [30] Wolf, R. M. et al. The SEE Study: safety, efficacy, and equity of implementing autonomous artificial intelligence for diagnosing diabetic retinopathy in youth. Diabetes Care 44, 781–787 (2021). sourceSupported

May combine two claims.

This care gap is a major source of health disparity, with racial and ethnic minorities, and under-resourced communities, having worse outcomes, and a disproportionately higher prevalence of DED.

“This care gap is a major source of health disparity, with racial and ethnic minorities, and under-resourced communities, having worse outcomes, and a disproportionately higher prevalence of DED2,5–9.”

• cites [2] Zhang, X. et al. Prevalence of diabetic retinopathy in the United States, 2005-2008. JAMA 304, 649–656 (2010). sourcePartly supported

  The Results report higher prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy among non-Hispanic Black and Mexican American individuals with diabetes than among non-Hispanic White individuals (page not provided). The Comment suggests that differences in screening and treatment may contribute, but does not establish the care gap as a major cause. The paper does not report DED prevalence or show worse outcomes in under-resourced communities. (LLM, conf=0.86)

  Experimental — accuracy not yet measured
• cites [5] Hill-Briggs, F. et al. Social determinants of health and diabetes: a scientific review. Diabetes Care 44, 258–279 (2020). sourcePartly supported

  The Introduction reports higher diabetes risk, complications, and mortality among racial and ethnic minority and low-income populations. The Health Care and Diabetes section documents disparities in care quality, including dilated eye examinations. Neither passage establishes that the care gap causes a higher prevalence of DED in those groups. (LLM, conf=0.86)

  Experimental — accuracy not yet measured
• cites [6] Effect of intensive diabetes treatment on the development and progression of long-term complications in adolescents with insulin-dependent diabetes mellitus: Diabetes Control and Complications Trial. Diabetes Control and Complications Trial Research Group. J. Pediatr. 125, 177–188 (1994). sourceNot checked — no full text of the source
• cites [7] Benoit, S. R., Swenor, B., Geiss, L. S., Gregg, E. W. & Saaddine, J. B. Eye care utilization among insured people with diabetes in the U.S., 2010-2014. Diabetes Care 42, 427–433 (2019). sourceNot checked — no full text of the source
• cites [8] Shi, Q., Zhao, Y., Fonseca, V., Krousel-Wood, M. & Shi, L. Racial disparity of eye examinations among the U.S. working-age population with diabetes: 2002-2009. Diabetes Care 37, 1321–1328 (2014). sourcePartly supported

  The Conclusions (Summary; page unavailable) report lower eye-examination rates among racial and ethnic minority patients with diabetes, and the Introduction reports higher diabetic-retinopathy prevalence in some minority groups. The paper does not establish that the care gap causes worse outcomes in under-resourced communities, and it does not address DED prevalence if DED means dry eye disease. Its Study Limitations state that the cross-sectional analysis could not establish causality. (LLM, conf=0.86)

  Experimental — accuracy not yet measured
• cites [9] Patel, D. et al. Social determinants of health and impact on screening, prevalence, and management of diabetic retinopathy in adults: a narrative review. J. Clin. Med. 11. https://doi.org/10.3390/jcm11237120 (2022). sourcePartly supported

  The Introduction and §3.5.2 report worse diabetes-related outcomes and reduced eye-care access among underserved and minority populations. Section 3 reports disproportionately higher prevalence of diabetic retinopathy in some ethnic minority groups. The paper addresses diabetic retinal disease, not DED, so it does not support the claimed higher prevalence of DED. Page numbers were not provided. (LLM, conf=0.88)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing).

The majority of the 34 million people with diabetes in the US have a DED screening care gap, due to a lack of access, and education around the need for a diabetic eye exam.

“the majority of the 34 million people with diabetes in the US have a DED screening care gap, due to a lack of access, and education around the need for a diabetic eye exam3,4.”

• cites [3] Zhang, X. et al. Vision health disparities in the United States by race/ethnicity, education, and economic status: findings from two nationally representative surveys. Am. J. Ophthalmol. 154, S53–62.e51 (2012). sourcePartly supported

  The Discussion (Vision Health Disparities and Public Health Implications; page not provided) identifies access and awareness barriers to eye care and says challenges remain in getting people at risk screened for diabetic retinopathy. It does not establish that a majority of the 34 million US residents with diabetes have a diabetic-eye-exam screening gap, or that lack of access and education explain such a gap in that population. (LLM, conf=0.86)

  Experimental — accuracy not yet measured
• cites [4] Hale, N. L., Bennett, K. J. & Probst, J. C. Diabetes care and outcomes: disparities across rural America. J. Community Health 35, 365–374 (2010). sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Autonomous AI allows real-time, point-of-care diagnosis, whether in the primary care, endocrine, or other outpatient setting, can be integrated into the diabetes care workflow, and was included in the ADA standards of care for DED screening in adults as of 2020.

“Autonomous AI allows real-time, point-of-care diagnosis, whether in the primary care, endocrine, or other outpatient setting, can be integrated into the diabetes care workflow, and was included in the ADA standards of care for DED screening in adults as of 202036.”

• cites [36] American Diabetes Association. Microvascular complications and foot care: standards of medical care in diabetes-2020. Diabetes Care 43, S135–S151 (2020). sourcePartly supported

  Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing).

Pigmentation of the retina could introduce bias in an AI system for the diabetic eye exam.

“it is important to consider pigmentation of the retina that could introduce bias in an AI system for the diabetic eye exam43.”

• cites [43] Abramoff, M., Tarver, E. M., Loyo-Barrios, N., Trujillo, S., Char, D. Considerations for addressing bias in artificial intelligence for health equity. NPJ Digit Med 6, 170 (2023). sourceCould not verify

  The Development phase discusses how unrepresentative training data can introduce bias, and the Access and monitoring phases discuss bias in AI-assisted diabetic-retinopathy care. Neither passage addresses retinal pigmentation as a source of bias. Searches for retinal pigmentation, pigment, and retinal-image characteristics found no passage making that connection. (LLM, conf=0.73)

  Experimental — accuracy not yet measured

Classified as probably not a substantial claim. May combine two claims.

The 100% care-gap closure rate by autonomous AI in this racially and ethnically diverse pediatric population could have the potential to reduce health disparities for vision loss from diabetes, furthering health equity.

“However, the 100% care-gap closure rate by autonomous AI in this racially and ethnically diverse pediatric population could have the potential to reduce health disparities for vision loss from diabetes, furthering health equity, and deserves further study42,43.”

• cites [42] Braveman, P. What are health disparities and health equity? We need to be clear. Public Health Reports (Washington, D.C.: 1974) 129, 5–8 (2014). sourcePartly supported

  The passage defining health equity and health disparities says that reducing disparities is evidence of progress toward health equity. It does not address autonomous AI, a 100% care-gap closure rate, or whether that intervention could reduce disparities in diabetes-related vision loss among children. (LLM, conf=0.85)

  Experimental — accuracy not yet measured
• cites [43] Abramoff, M., Tarver, E. M., Loyo-Barrios, N., Trujillo, S., Char, D. Considerations for addressing bias in artificial intelligence for health equity. NPJ Digit Med 6, 170 (2023). sourcePartly supported

  The Introduction says AI systems may help address health disparities and cites work on autonomous AI for diabetic retinopathy in youth. It does not report a 100% care-gap closure rate or establish that rate in the racially and ethnically diverse pediatric population described in the claim. (LLM, conf=0.84)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The randomization sequence was created by a statistician unaffiliated with the study to ensure masking to the randomization scheme and was implemented by REDCAP's randomization software based on the participant's location.

“This randomization sequence was created by a statistician unaffiliated with the study to ensure masking to the randomization scheme and was implemented by REDCAP’s randomization software based on the participant’s location52,53.”

• cites [52] Harris, P. A. et al. The REDCap consortium: building an international community of software platform partners. J. Biomed. Inform. 95, 103208 (2019). sourcePartly supported

  The Phase 5 section says REDCap added a participant-randomization module, supporting the identification of REDCap as randomization software. It does not describe this study’s sequence, an unaffiliated statistician, masking, or randomization based on participant location; searches for those details found no supporting passage. No page number is available in our copy. (LLM, conf=0.88)

  Experimental — accuracy not yet measured
• cites [53] Harris, P. A. et al. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inform. 42, 377–381 (2009). sourceCould not verify

  The Methods section describes REDCap as a system for collecting and managing research data, but does not describe a randomization module or location-based assignment. Searches for a randomization sequence, masking, and assignment by location found no passage addressing those details or the statistician’s role. (LLM, conf=0.76)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing).

The IDx-DR autonomous AI system was US FDA De Novo authorized in 2018 for adults with diabetes.

“The autonomous AI system (IDx-DR, Digital Diagnostics, Coralville, Iowa, USA) for diagnosing diabetic eye disease (DED) was US FDA De Novo authorized (“FDA approval”) in 2018 for adults with diabetes15.”

• cites [15] Bjornstad, P. et al. Long-term complications in youth-onset type 2 diabetes. N. Engl J. Med. 385, 416–426 (2021). sourceCould not verify

  The Methods section, “Diabetic Eye Disease” (page not provided), describes fundus photography graded at a centralized reading center, not the IDx-DR system. Searches for IDx-DR, FDA De Novo authorization, and autonomous AI found no passage in our copy addressing the claimed 2018 authorization for adults with diabetes. (LLM, conf=0.77)

  Experimental — accuracy not yet measured

Classified as probably not a substantial claim.

The IDx-DR system diagnoses referable DED, defined as ETDRS level 35 or higher, clinically significant macular edema, and/or center-involved macular edema, which requires further management or treatment by an ophthalmologist or retina specialist.

“The system diagnoses specific levels of diabetic retinopathy and diabetic macular edema (Early Treatment of Diabetic Retinopathy Study level 35 and higher, clinically significant macular edema, and or center-involved macular edema)32,33, referred to as “referable DED”34, that requires further management or treatment by an ophthalmologist or retina specialist.”

• cites [32] Verbraak, F. D. et al. Diagnostic accuracy of a device for the automated detection of diabetic retinopathy in a primary care setting. Diabetes Care 42, 651–656 (2019). sourcePartly supported

  Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  Experimental — accuracy not yet measured
• cites [33] Abràmoff, M. D. et al. Foundational considerations for artificial intelligence using ophthalmic images. Ophthalmology 129, e14–e32 (2022). sourceCould not verify

  Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.

  Experimental — accuracy not yet measured
• cites [34] NCQA. HEDIS Measurement Year 2020 and Measurement Year 2021. Technical Specifications for Health Plans. 2L (NCQA, 2020).Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

In prior studies of youth with diabetes, the diagnosability of autonomous AI in youth was 97.5%, with 85.7% sensitivity and 79.3% specificity in detecting DED, with no difference in diagnosability across demographic groups.

“In prior studies of youth with diabetes, we demonstrated that the diagnosability of autonomous AI in youth was 97.5%, with 85.7% sensitivity and 79.3% specificity in detecting DED, with no difference in diagnosability across demographic groups30.”

• cites [30] Wolf, R. M. et al. The SEE Study: safety, efficacy, and equity of implementing autonomous artificial intelligence for diagnosing diabetic retinopathy in youth. Diabetes Care 44, 781–787 (2021). sourcePartly supported

  The Results report 97.5% diagnosability, 85.7% sensitivity, and 79.3% specificity in the youth cohort, with no significant effect of race, ethnicity, age, or sex on diagnosability. The sensitivity and specificity, however, are specifically for detecting more-than-mild diabetic retinopathy, not diabetic eye disease broadly. (LLM, conf=0.84)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Commonly reported barriers to screening include miscommunication regarding the need for a diabetic eye exam, time for an additional doctor's visit, and transportation barriers.

“Commonly reported barriers to screening include miscommunication regarding the need for a diabetic eye exam, time for an additional doctor’s visit, and transportation barriers17,18.”

• cites [17] Thomas, C. G. et al. Racial/ethnic disparities and barriers to diabetic retinopathy screening in youths. JAMA Ophthalmol 139, 791–795 (2021). sourceNot checked — no full text of the source
• cites [18] Bruggeman, B. et al. Barriers to retinopathy screening in youth and young adults with type 1 diabetes. Pediatr. Diabetes 22, 469–473 (2021). sourcePartly supported

  The Results report missed work or school (20.7%), not thinking an exam was necessary (7.7%), lack of time among other barriers (1.8%), and transportation difficulties (2.2%). The Discussion says lack of a provider recommendation was not common in this population. The paper therefore supports time-related and transportation barriers, but does not establish miscommunication about the need for an exam as a commonly reported barrier. (LLM, conf=0.88)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

In this study, participants' eyes were not pharmacologically dilated, as pilot studies found that pharmacologic dilation is unnecessary in youth.

“In this study, the participant’s eyes were not pharmacologically dilated, as pilot studies found that pharmacologic dilation is unnecessary in youth30.”

• cites [30] Wolf, R. M. et al. The SEE Study: safety, efficacy, and equity of implementing autonomous artificial intelligence for diagnosing diabetic retinopathy in youth. Diabetes Care 44, 781–787 (2021). sourcePartly supported

  The Research Design and Methods section says, “No pupil dilation was used,” and the paper states that digital fundus photography can be performed without dilation (Abstract; page unavailable). Our copy does not say that pilot studies established pharmacologic dilation was unnecessary in youth, so the stated reason is unsupported. (LLM, conf=0.86)

  Experimental — accuracy not yet measured

May combine two claims.

Only 35-72% of diabetic youth undergo recommended screening exams, with even higher care gap rates in minority and lower socioeconomic background youth.

“However, only 35-72% of diabetic youth undergo recommended screening exams, with even higher care gap rates in minority and lower socioeconomic background youth10,17.”

• cites [10] Wang, S. Y. et al. Ophthalmic screening patterns among youths with diabetes enrolled in a large US managed care network. JAMA Ophthalmol 135, 432–438 (2017). sourcePartly supported

  The Discussion reports that 42.2% of youths with type 2 diabetes and 64.9% with type 1 diabetes received an eye examination within six years of diagnosis, and identifies lower examination rates among Black, Latino, and less affluent youths. It also cites rates of 35% and 70–72% from prior studies, but the 35% figure measures referrals rather than completed exams, while the 70–72% figure measures examinations around a transition in care rather than adherence to recommended screening. No page numbers are available in our copy. (LLM, conf=0.86)

  Experimental — accuracy not yet measured
• cites [17] Thomas, C. G. et al. Racial/ethnic disparities and barriers to diabetic retinopathy screening in youths. JAMA Ophthalmol 139, 791–795 (2021). sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

This particular autonomous AI has been shown not to have racial bias.

“while this particular autonomous AI has been shown not to have racial bias”

• cites [43] Abramoff, M., Tarver, E. M., Loyo-Barrios, N., Trujillo, S., Char, D. Considerations for addressing bias in artificial intelligence for health equity. NPJ Digit Med 6, 170 (2023). sourceCould not verify

  The 'Measuring bias in AI systems' section discusses testing diagnostic accuracy across subgroups, and the References list a trial of an autonomous diabetic-retinopathy AI. Neither passage says that this particular AI was tested and found free of racial bias. Searches for 'no racial bias' and race-specific validation results found no such finding in our copy. (LLM, conf=0.72)

  Experimental — accuracy not yet measured

May combine two claims.

This system can be implemented in a multidisciplinary diabetes clinic and has the potential to increase DED screening rates in underserved youth, while also being cost-savings to patients and caregivers.

“We have also shown that this system can be implemented in a multidisciplinary diabetes clinic and has the potential to increase DED screening rates in underserved youth, while also being cost-savings to patients and caregivers30,31.”

• cites [30] Wolf, R. M. et al. The SEE Study: safety, efficacy, and equity of implementing autonomous artificial intelligence for diagnosing diabetic retinopathy in youth. Diabetes Care 44, 781–787 (2021). sourceSupported
• cites [31] Wolf, R. M., Channa, R., Abramoff, M. D. & Lehmann, H. P. Cost-effectiveness of autonomous point-of-care diabetic retinopathy screening for pediatric patients with diabetes. JAMA Ophthalmol. 138, 1063–1069 (2020). sourcePartly supported

  The Discussion (page unavailable) says point-of-care screening can be performed in a diabetes clinic, potentially increasing screening and sparing families another visit. The Results (page unavailable) find patient cost savings only when adherence to conventional eye examinations reaches at least 23%; at baseline, modeled patient payments were higher with AI screening. The paper does not establish implementation in a multidisciplinary clinic or specifically assess underserved youth. (LLM, conf=0.85)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing).

The CONSORT requirements for RCT were followed.

“The CONSORT requirements for RCT were followed51.”

• cites [51] Schulz, K. F., Altman, D. G. & Moher, D. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ (Clinical Research ed.) 340, c332 (2010). sourceSupported

May combine two claims.

The baseline data in this study confirmed racial, ethnic, and socioeconomic gap closure disparities, similar to other studies demonstrating the wide range of disparities associated with social determinants of health in diabetic retinopathy screening.

“The baseline data in this study confirmed these racial, ethnic, and socioeconomic gap closure disparities, similar to other studies demonstrating the wide range of disparities associated with social determinants of health in diabetic retinopathy screening9,41.”

• cites [9] Patel, D. et al. Social determinants of health and impact on screening, prevalence, and management of diabetic retinopathy in adults: a narrative review. J. Clin. Med. 11. https://doi.org/10.3390/jcm11237120 (2022). sourceSupported
• cites [41] Lock, L. J., Channa, R., Brennan, M. B., Cao, Y. & Liu, Y. Effect of health system on the association of rurality and level of disadvantage with receipt of diabetic eye screening. BMJ Open Diabetes Res. Care 10. https://doi.org/10.1136/bmjdrc-2022-003174 (2022). sourcePartly supported

  Split into 2 atoms — 1 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable, 1 not checked. Expand the claim to see per-atom verdicts.

  Experimental — accuracy not yet measured

May combine two claims.

In the ACCESS trial, the diabetic eye exam completion rate within 6 months was 100% (81/81; 95% CI 95.5-100%) in the autonomous AI intervention group versus 22% (18/82; 95% CI 14.2-32.4%) in the control group, a significant difference (p < 0.001).

“Diabetic eye exam completion rate was significantly higher (100%, 95%CI: 95.5%, 100%) in the intervention group (n = 81) than the control group (n = 83) (22%, 95%CI: 14.2%, 32.4%)(p < 0.001).”

• Also stated in: § Primary outcome · plain

  “in the intervention arm, 81/81 (100%) participants completed their diabetic eye exams, so the primary care gap closure rate was 100% (95%CI: 96%, 100%). All images were diagnostic for the AI system (output was either “DED present” or “DED absent”). In the control arm, 18/82 completed the diabetic eye exam within 6 months, so the primary care gap closure rate was 22% (95%CI: 14%, 32%).”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

In the ACCESS trial, participants reported high satisfaction with autonomous AI: 92.5% were satisfied with the length of time to complete the exam, and 96% were satisfied with the experience.

“Participants reported a high level of satisfaction with autonomous AI, 92.5% were satisfied with the length of time it took to complete the exam, and 96% were satisfied with the experience.”

May combine two claims.

In univariate analysis, participants without a prior eye exam were more likely to be Black, Hispanic, have household income <$50,000, Medicaid insurance, parental education up to high school completion, and a shorter duration of diabetes.

“Those without a prior eye exam were more likely to be Black (p = <0.001), Hispanic (p = 0.02), have household income of less than $50,000 (p = 0.005), Medicaid insurance (p < 0.001), parental education of up to high school completion (p = 0.02), and a shorter duration of diabetes (p < 0.001) in univariate analysis.”

May combine two claims.

At baseline, 79% of participants reported having had a prior diabetic eye exam at some time in the past, and 35/164 (21%) had not (i.e., had a care gap).

“Regarding disparities in ever having a prior diabetic eye exam, 79% of participants reported having had such an exam at some time in the past (but per inclusion criteria, not in the prior 6 months) and 35/164 (21%) did not (i.e., had a care gap).”

May combine two claims.

In the control arm, the follow-through completion rate with an eye care provider was 22% (18/82), and none had DED.

“In the control arm, 18 participants visited the ECP, for a follow-through completion rate of 22% (95%CI: 14%, 32%) and none had DED.”

May combine two claims.

The difference in care-gap closure between control and intervention groups was 78% (95% CI 69-87%) and statistically significant (p < 0.001).

“The difference of 78% (95% CI: 69%, 87%) in gap closure between control and intervention groups was statistically significant (p < 0.001).”

May combine two claims.

The difference in follow-through completion rates between control and intervention groups was 42% (95% CI 21-63%) and significant (p < 0.001).

“The difference of 42% (95%CI: 21%, 63%) in follow-through completion rates between control and intervention groups was significant (p < 0.001).”

May combine two claims.

Of intervention-arm participants, 85% reported they would choose to do the AI-based eye exam in the future, and 57% would choose an ECP-based diabetic eye exam.

“Of those who were in the intervention arm, 85% reported they would choose to do the AI-based eye exam in the future, and only 57%, an ECP-based diabetic eye exam (Table 4).”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

There were differences in diabetes type, race, parental education, and diabetes management characteristics between the two recruitment sites, but glycemic control was similar across sites.

“There were differences in diabetes type, race, parental education, and diabetes management characteristics between the two recruitment sites, but glycemic control (measured by hemoglobin A1c) was similar across sites.”

May combine two claims.

In the intervention arm, follow-through was significantly higher at 64%, as participants with "DED present" results were immediately informed of the diagnosis and provided education.

“However, in the intervention arm, follow-through was significantly higher at 64%, as participants with “DED present” results were immediately informed of the diagnosis and provided education.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The autonomous AI used is not FDA-cleared for use in ages 21 and under.

“Limitations of the present study are that the autonomous AI used is not FDA-cleared for use in ages 21 and under”

Classified as probably not a substantial claim. May combine two claims.

The ACCESS study was a pre-registered, rigorously designed randomized controlled trial.

“To test this hypothesis, we designed ACCESS (AI for Childrens’ diabetiC Eye examS Study), a pre-registered, rigorously designed randomized control trial (RCT)”

• Also stated in: § Discussion · plain

  “Strengths of the present ACCESS study are its rigorous hypothesis-testing, pre-registered RCT design”

Probably not a substantial claim: may not pass S3 (load-bearing).

A mixed multilevel multivariable logistic regression analysis, using site as a nesting level, was performed to examine the relationship between demographic characteristics and the odds of having a previous diabetic eye exam amongst the entire study cohort, adjusting for known covariates associated with DED and site.

“A mixed multilevel multivariable logistic regression analysis, using the site as a nesting level to account for the clustering of observations, was performed in order to examine the relationship between demographic characteristics and the odds of having a previous diabetic eye exam amongst the entire study cohort, adjusting for known covariates associated with DED and site.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

At the time of the DSMB meeting on 9/16/2022, all participants had already been enrolled in the trial, and the DSMB determined that there were no safety concerns and the study should continue to completion.

“At the time of the DSMB meeting on 9/16/2022, all participants had already been enrolled in the trial, and the DSMB determined that there were no safety concerns and the study should continue to completion.”

May combine two claims.

Since randomization and study visits occurred at the same time, there was little risk of attrition and the sample size was not expanded to account for attrition.

“Since randomization and study visits occurred at the same time there was little risk of attrition and thus the sample size was not expanded to account for attrition.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The tenets of the Declaration of Helsinki were followed.

“the tenets of the Declaration of Helsinki were followed”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Stratified randomization by site was used to prevent selection bias and ensure sample size balance between groups and sites.

“To prevent selection bias and ensure sample size balance between the groups and sites, stratified randomization (by site) was used”

Probably not a substantial claim: may not pass S3 (load-bearing).

An independent Data Safety and Monitoring Board was established.

“an independent Data Safety and Monitoring Board was established”

• Also stated in: § Data governance · plain

  “an independent Data Safety and Monitoring Board was established to protect the interests of study participants and to preserve the integrity and credibility of the study data”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

ACCESS was a hypothesis-driven, pre-registered, prospective parallel RCT with a 1:1 allocation ratio.

“ACCESS is a hypothesis-driven, pre-registered, prospective parallel, RCT with a 1:1 allocation ratio”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

All parties were masked to the allocation until the participant was randomized in the study, and then all parties were unmasked.

“All parties were masked to the allocation until the participant was randomized in the study, and then all parties were unmasked.”

Classified as probably not a substantial claim. May combine two claims.

All images were also overread by a board-certified retina specialist to ensure safety and that no cases of disease would be missed.

“To ensure safety and that no cases of disease would be missed, all images were also overread by a board-certified retina specialist.”

Classified as probably not a substantial claim.

With this autonomous AI system, a medical diagnosis is made independently by the system without human oversight.

“With this autonomous AI system, a medical diagnosis is made independently by the system without human oversight.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The study was approved by the Johns Hopkins IRB.

“The study was approved by the Johns Hopkins IRB”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

The scripted clinical education given to participants above the standard of care in the control arm maximized the likelihood of care gap closure in the control arm.

“as well as the scripted clinical education given to participants above the standard of care in the control arm, to maximize the likelihood of care gap closure in the control arm.”

May combine two claims.

Sharing a diagnosis of referable disease at the point-of-care potentially improves visual outcomes in this vulnerable population and advances health equity in youth with diabetes.

“potentially improving visual outcomes in this vulnerable population and advancing health equity in youth with diabetes”

• AssumptionThe authors state this as an assumption: Control-arm patients who arrive at the ECP for screening remain at the ECP for management or treatment when found to have DED.

Probably not a substantial claim: may not pass S3 (load-bearing).

Participants were randomized in permutated block schedules of 4 and 6.

“participants were randomized in permutated block schedules of 4 and 6”

Probably not a substantial claim: may not pass S3 (load-bearing).

If ETDRS level is 20 or lower and no macular edema is present, appropriate management is to retest in 12 months.

“If the ETDRS level is 20 or lower and no macular edema is present, appropriate management is to retest in 12 months35.”

• cites [35] Center for Medicare and Medicaid Services. Proposal to Establish Values for Remote Retinal Imaging (CPT code 92229). Pages 56ff (2021).Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

There were no changes in trial design or outcome after trial commencement.

“There were no changes in trial design or outcome after trial commencement.”

Classified as probably not a substantial claim. May combine two claims.

The American Diabetes Association (ADA) and American Academy of Ophthalmology (AAO) recommend DED screening in T1D within 3-5 years of diagnosis and age greater than 11 years, and in T2D at the time of diagnosis in youth.

“The American Diabetes Association (ADA) and American Academy of Ophthalmology (AAO) recommend DED screening in T1D within 3–5 years of diagnosis and age greater than 11 years, and in T2D at the time of diagnosis in youth16.”

• cites [16] American Diabetes Association. Children and adolescents: standards of medical care in diabetes-2021. Diabetes Care 44, S180–s199 (2021). sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S4 (based). May combine two claims.

The IDx-DR autonomous AI is not labeled for youth <22 years, as currently no autonomous AI for DED has been cleared for a pediatric population.

“The IDx-DR autonomous AI is not labeled for youth <22 years, as currently no autonomous AI for DED has been cleared for a pediatric population.”

Probably not a substantial claim: may not pass S3 (load-bearing).

ACCESS was pre-registered on ClinicalTrials.gov (NCT05131451).

“ACCESS was pre-registered on ClinicalTrials.gov (NCT05131451).”

Classified as probably not a substantial claim.

Within each block, participants were randomized with a 1:1 allocation ratio to the control group and intervention group.

“Within each block, participants were randomized with a 1:1 allocation ratio to the control group and intervention group.”

Classified as probably not a substantial claim.

The primary outcome was pre-specified as the proportion of participants who completed a documented diabetic eye exam in each arm ('primary care gap closure rate').

“the pre-specified primary outcome was defined as the proportion of participants who completed a documented diabetic eye exam in each arm (“primary care gap closure rate”)”

Classified as probably not a substantial claim.

The secondary outcome was pre-specified as the proportion of participants who completed follow-through at the ECP in each arm ('follow-through completion rate').

“The pre-specified secondary outcome (“follow-through completion rate”) was defined as the proportion of participants who completed follow-through at the ECP in each arm.”

• AssumptionThe authors state this as an assumption: Control-arm patients who arrive at the ECP for screening remain at the ECP for management or treatment when found to have DED.