Glucose management indicator (GMI): a new term for estimating A1C from continuous glucose monitoring
Open accessAbstract
While A1C is well established as an important risk marker for diabetes complications, with the increasing use of continuous glucose monitoring (CGM) to help facilitate safe and effective diabetes management, it is important to understand how CGM metrics, such as mean glucose, and A1C correlate. Estimated A1C (eA1C) is a measure converting the mean glucose from CGM or self-monitored blood glucose readings, using a formula derived from glucose readings from a population of individuals, into an estimate of a simultaneously measured laboratory A1C. Many patients and clinicians find the eA1C to be a helpful educational tool, but others are often confused or even frustrated if the eA1C and laboratory-measured A1C do not agree. In the U.S., the Food and Drug Administration determined that the nomenclature of eA1C needed to change. This led the authors to work toward a multipart solution to facilitate the retention of such a metric, which includes renaming the eA1C the glucose management indicator (GMI) and generating a new formula for converting CGM-derived mean glucose to GMI based on recent clinical trials using the most accurate CGM systems available. The final aspect of ensuring a smooth transition from the old eA1C to the new GMI is providing new CGM analyses and explanations to further understand how to interpret GMI and use it most effectively in clinical practice. This Perspective will address why a new name for eA1C was needed, why GMI was selected as the new name, how GMI is calculated, and how to understand and explain GMI if one chooses to use GMI as a tool in diabetes education or management.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Mean glucose was 154±26.8 mg/dL, corresponding to an HbA1c of approximately 7%.
“The mean glucose of 154±26.8 mg/dL corresponded with an HbA1c of approximately 7%.14”
From Clinical utility of the twiist Automated Insulin Delivery System: first study with twiist (2026) · cites this paper as [14]
In “How Is GMI Calculated?” (page unavailable), the paper gives a formula under which a mean glucose of 154 mg/dL yields a GMI of approximately 7%. It does not report the stated 154±26.8 mg/dL mean and variation, and it cautions elsewhere that GMI can differ from laboratory-measured HbA1c. (LLM, conf=0.86)
Experimental — accuracy not yet measuredUsing the standardized report, clinicians can address glucose variability or use GMI to discuss discrepancies between CGM-derived glucose exposure and laboratory-measured A1C.
“Using the standardized report the clinician can also address glucose variability (e.g., %CV metric) (75) or use glucose management indicator (GMI) metric (43) to discuss the possible discrepancies noted in glucose exposure derived from CGM data versus the individual's laboratory-measured A1C (40; 76).”
From Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range (2019) · cites this paper as [75]
If HbA1c is lower than a concurrent GMI measure, HbA1c can be interpreted to underestimate average blood glucose by the difference in measurements, allowing adjustment of HbA1c targets accordingly.
“For example, if HbA1c is lower than a concurrent GMI measure, the HbA1c can be interpreted to underestimate average blood glucose by the difference in measurements, allowing adjustment of HbA1c targets accordingly. 230,231”
From KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease (2022) · cites this paper as [230]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
International consensus on use of continuous glucose monitoring
Danne, T., Nimri, R., Battelino, T., Bergenstal, R. M., Close, K. L., DeVries, J. H., Garg, S., Heinemann, L., Hirsch, I., Amiel, S. A., et al. (2017) · Diabetes Care · doi:10.2337/dc17-1600 · Cited by 2,069 · cited 1× · see citing works
“An international consensus group recently included the eA1C in its list of core CGMmetrics recommended for inclusion in all standard CGM reports (2).”
BackgroundSelf-citationTranslating the A1C assay into estimated average glucose values
Nathan, D. M., Kuenen, J., Borg, R., Zheng, H., Schoenfeld, D., Heine, R. J., & for the A1c-Derived Average Glucose (ADAG) Study Group (2008) · Diabetes Care · doi:10.2337/dc08-0545 · Cited by 1,812 · see citing works
Miller, K. M., Foster, N. C., Beck, R. W., Bergenstal, R. M., DuBose, S. N., DiMeglio, L. A., Maahs, D. M., & Tamborlane, W. V. (2015) · Diabetes Care · doi:10.2337/dc15-0078 · Cited by 1,254 · see citing works
Self-citationBeck, R. W., Riddlesworth, T., Ruedy, K., Ahmann, A., Bergenstal, R., Haller, S., Kollman, C., Kruger, D., McGill, J. B., Polonsky, W., et al. (2017) · JAMA · doi:10.1001/jama.2016.19975 · Cited by 1,185 · see citing works
Self-citationBeck, R. W., Riddlesworth, T. D., Ruedy, K., Ahmann, A., Haller, S., Kruger, D., McGill, J. B., Polonsky, W., Price, D., Aronoff, S., et al. (2017) · Annals of Internal Medicine · doi:10.7326/m16-2855 · Cited by 635 · see citing works
Self-citation4. Lifestyle Management: <i>Standards of Medical Care in Diabetes—2018</i>
American Diabetes Association (2018) · Diabetes Care · doi:10.2337/dc18-s004 · Cited by 563 · see citing works
Heinemann, L., Freckmann, G., Ehrmann, D., Faber-Heinemann, G., Guerra, S., Waldenmaier, D., & Hermanns, N. (2018) · The Lancet · doi:10.1016/s0140-6736(18)30297-6 · Cited by 522 · cited 1× · see citing works
“To validate this formula, CGM data from the HypoDE study (10) (141 individuals with T1D for whom 4 weeks of data were collected with the Dexcom G5 sensor prior to A1C measurement) were analyzed to compute the regression equation to estimate A1C from the CGMmeasured mean glucose concentration.”
MethodSelf-citationThe fallacy of average: how using HbA1c alone to assess glycemic control can be misleading
Beck, R. W., Connor, C. G., Mullen, D. M., Wesley, D. M., & Bergenstal, R. M. (2017) · Diabetes Care · doi:10.2337/dc17-0636 · Cited by 477 · see citing works
Self-citationAleppo, G., Ruedy, K. J., Riddlesworth, T. D., Kruger, D. F., Peters, A. L., Hirsch, I., Bergenstal, R. M., Toschi, E., Ahmann, A. J., Shah, V. N., et al. (2017) · Diabetes Care · doi:10.2337/dc16-2482 · Cited by 329 · see citing works
Self-citationBergenstal, R. M., Ahmann, A. J., Bailey, T., Beck, R. W., Bissen, J., Buckingham, B., Deeb, L., Dolin, R. H., Garg, S. K., Goland, R., et al. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0051 · Cited by 307 · see citing works
Self-citationOptimal Sampling Duration for Continuous Glucose Monitoring to Determine Long-Term Glycemic Control
Riddlesworth, T. D., Beck, R. W., Gal, R. L., Connor, C. G., Bergenstal, R. M., Lee, S., & Willi, S. M. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0455 · Cited by 266 · cited 1× · see citing works
“A recent study using modern CGM technology concluded that 10–14 days of CGM data provide a good estimate of CGM metrics for a 3-month period (1).”
BackgroundSelf-citationPeters, A. L., Ahmann, A. J., Battelino, T., Evert, A., Hirsch, I. B., Murad, M. H., Winter, W. E., & Wolpert, H. (2016) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2016-2534 · Cited by 250 · see citing works
The use of language in diabetes care and education
Dickinson, J. K., Guzman, S. J., Maryniuk, M. D., O’Brian, C. A., Kadohiro, J. K., Jackson, R. A., D’Hondt, N., Montgomery, B., Close, K. L., & Funnell, M. M. (2017) · Diabetes Care · doi:10.2337/dci17-0041 · Cited by 241 · cited 3× · see citing works
“Multiple physicians, researchers, diabetes educators, and individuals with diabetes quoted articles from Dickinson et al., including “The Use of Language in Diabetes Care and Education” (5), in support of their preference for the word “management.””
BackgroundInfluentialSelf-citationFonseca, V. A., Grunberger, G., Anhalt, H., Bailey, T. S., Blevins, T., Garg, S. K., Handelsman, Y., Hirsch, I. B., Orzeck, E. A., Roberts, V. L., et al. (2016) · Endocrine Practice · doi:10.4158/ep161392.cs · Cited by 195 · see citing works
Self-citationHemoglobin A1c and Mean Glucose in Patients With Type 1 Diabetes
Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group (2011) · Diabetes Care · doi:10.2337/dc10-1054 · Cited by 60 · see citing works
Self-citationContinuous glucose monitoring: transforming diabetes management step by step
Bergenstal, R. M. (2018) · The Lancet · doi:10.1016/s0140-6736(18)30290-3 · Cited by 33 · see citing works
Self-citation
Flagged first, then by citations
2 of the 18 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Battelino, T., Danne, T., Bergenstal, R. M., Amiel, S. A., Beck, R., Biester, T., Bosi, E., Buckingham, B. A., Cefalu, W. T., Close, K. L., et al. (2019) · Diabetes Care · doi:10.2337/dci19-0028 · Cited by 4,032 · see citing works
“Glucose management indicator (GMI) (75)”
BackgroundInfluentialSelf-citationKDIGO 2020 Clinical practice guideline for diabetes management in chronic kidney disease
de Boer, I. H., Caramori, M. L., Chan, J. C. N., Heerspink, H. J. L., Hurst, C., Khunti, K., Liew, A., Michos, E. D., Navaneethan, S. D., Olowu, W. A., et al. (2020) · Kidney International · doi:10.1016/j.kint.2020.06.019 · Cited by 1,399 · see citing works
KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease
Rossing, P., Caramori, M. L., Chan, J. C. N., Heerspink, H. J. L., Hurst, C., Khunti, K., Liew, A., Michos, E. D., Navaneethan, S. D., Olowu, W. A., et al. (2022) · Kidney International · doi:10.1016/j.kint.2022.06.008 · Cited by 1,373 · see citing works
Continuous glucose monitoring and metrics for clinical trials: an international consensus statement
Battelino, T., Alexander, C. M., Amiel, S. A., Arreaza-Rubin, G., Beck, R. W., Bergenstal, R. M., Buckingham, B. A., Carroll, J., Ceriello, A., Chow, E., et al. (2023) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(22)00319-9 · Cited by 831 · see citing works
Self-citationThe relationships between time in range, hyperglycemia metrics, and HbA1c
Beck, R. W., Bergenstal, R. M., Cheng, P., Kollman, C., Carlson, A. L., Johnson, M. L., & Rodbard, D. (2019) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818822496 · Cited by 479 · see citing works
Self-citationConsensus report: definition and interpretation of remission in type 2 diabetes
Riddle, M. C., Cefalu, W. T., Evans, P. H., Gerstein, H. C., Nauck, M. A., Oh, W. K., Rothberg, A. E., le Roux, C. W., Rubino, F., Schauer, P., et al. (2021) · Diabetes Care · doi:10.2337/dci21-0034 · Cited by 464 · see citing works
“In some circumstances, an eA1C or GMI <6.5% can be considered an equivalent criterion.”
InfluentialSelf-citationSamson, S. L., Vellanki, P., Blonde, L., Christofides, E. A., Galindo, R. J., Hirsch, I. B., Isaacs, S. D., Izuora, K. E., Low Wang, C. C., Twining, C. L., et al. (2023) · Endocrine Practice · doi:10.1016/j.eprac.2023.02.001 · Cited by 375 · see citing works
Grunberger, G., Sherr, J., Allende, M., Blevins, T., Bode, B., Handelsman, Y., Hellman, R., Lajara, R., Roberts, V. L., Rodbard, D., et al. (2021) · Endocrine Practice · doi:10.1016/j.eprac.2021.04.008 · Cited by 288 · see citing works
Self-citationde Boer, I. H., Caramori, M. L., Chan, J. C. N., Heerspink, H. J. L., Hurst, C., Khunti, K., Liew, A., Michos, E. D., Navaneethan, S. D., Olowu, W. A., et al. (2020) · Kidney International · doi:10.1016/j.kint.2020.06.024 · Cited by 278 · see citing works
“CGM may be useful to index hemoglobin A1c values for patients in whom hemoglobin A1c is not concordant with directly measured blood glucose levels or clinical symptoms.(11,12) In addition, CGM and self-monitoring of blood glucose can be used for short-term titration of treatments, prevention of hypoglycemia, and impro…”
MethodCollyns, O. J., Meier, R. A., Betts, Z. L., Chan, D. S. H., Frampton, C., Frewen, C. M., Hewapathirana, N. M., Jones, S. D., Roy, A., Grosman, B., et al. (2021) · Diabetes Care · doi:10.2337/dc20-2250 · Cited by 276 · see citing works
“This mean SG during AHCL equates to a glucose management indicator (16) of 6.”
BackgroundAdvances in technology for management of type 1 diabetes
Beck, R. W., Bergenstal, R. M., Laffel, L. M., & Pickup, J. C. (2019) · The Lancet · doi:10.1016/s0140-6736(19)31142-0 · Cited by 242 · see citing works
Self-citationConsensus report: definition and interpretation of remission in type 2 diabetes
Riddle, M. C., Cefalu, W. T., Evans, P. H., Gerstein, H. C., Nauck, M. A., Oh, W. K., Rothberg, A. E., le Roux, C. W., Rubino, F., Schauer, P., et al. (2021) · Diabetologia · doi:10.1007/s00125-021-05542-z · Cited by 121 · see citing works
Self-citationReal-World Performance of the MiniMed™ 780G System: First Report of Outcomes from 4120 Users
Silva, J. D., Lepore, G., Battelino, T., Arrieta, A., Castañeda, J., Grossman, B., Shin, J., & Cohen, O. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2021.0203 · Cited by 229 · see citing works
“Users with ‡10 days of sensor glucose (SG) data after AHCL was enabled for the first time (post-AHCL) were included in the analysis, based on previous publications using a similar duration of time to estimate or determine CGM-derived metrics.(13,14) All data available were included, whether the system was in AHCL cont…”
MethodA View Beyond HbA1c: Role of Continuous Glucose Monitoring
Chehrehgosha, H., Khamseh, M. E., Malek, M., Hosseinpanah, F., & Ismail‐Beigi, F. (2019) · Diabetes Therapy · doi:10.1007/s13300-019-0619-1 · Cited by 240 · see citing works
Glycemic Monitoring and Management in Advanced Chronic Kidney Disease
Galindo, R. J., Beck, R. W., Scioscia, M. F., Umpierrez, G. E., & Tuttle, K. R. (2020) · Endocrine Reviews · doi:10.1210/endrev/bnaa017 · Cited by 208 · see citing works
Self-citation7. Diabetes Technology: <i>Standards of Medical Care in Diabetes—2019</i>
American Diabetes Association (2019) · Diabetes Care · doi:10.2337/dc19-s007 · Cited by 179 · see citing works
“This provided a new way to use CGM data to estimate A1C (63).”
MethodPositioning time in range in diabetes management
Advani, A. (2020) · Diabetologia · doi:10.1007/s00125-019-05027-0 · Cited by 170 · see citing works
“The data analysis tools offer patients and healthcare professionals a wide range of metrics of glucose control quality, including number of days worn, percentage of time CGM is active, mean glucose, glucose management indicator (GMI; formerly estimated HbA1c [16]), glycaemic variability (%CV) and TIR [17].”
MethodChamberlain, J. J., Doyle-Delgado, K., Peterson, L., & SKOLNIK, N. (2019) · Annals of Internal Medicine · doi:10.7326/m19-1638 · Cited by 54 · see citing works
“Recently, estimated HbA1c was renamed the glucose management indicator (24).”
BackgroundMayeda, L., Katz, R., Ahmad, I., Bansal, N., Batacchi, Z., Hirsch, I. B., Robinson, N., Trence, D. L., Zelnick, L., & de Boer, I. H. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-000991 · Cited by 162 · see citing works
Lum, J. W., Bailey, R. J., Barnes-Lomen, V., Naranjo, D., Hood, K. K., Lal, R. A., Arbiter, B., Brown, A. S., DeSalvo, D. J., Pettus, J., et al. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0535 · Cited by 150 · see citing works
“To be included in the cohort for analysis, participants had to (1) not have started Loop, or used it for <7 days at the time of enrollment; (2) provided at least 50 records of Loop basal insulin data (represents 4–8 h of Loop use) or at least 1 Loop device issue report after starting Loop; and (3) provided at least 33…”
MethodSelf-citationArrieta, A., Battelino, T., Scaramuzza, A. E., Silva, J. D., Castañeda, J., Cordero, T. L., Shin, J., & Cohen, O. (2022) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14714 · Cited by 157 · see citing works
“While there is a clinically important difference between HbA1c and the GMI,(35) the latter provides a validated correlation between CGM metrics and managed glycaemia over time.(36) As GMI has become a CGM-derived standard for glycaemic control, it was used as a surrogate for HbA1c for determining treatment goal achiev…”
MethodNavaneethan, S. D., Zoungas, S., Caramori, M. L., Chan, J. C. N., Heerspink, H. J. L., Hurst, C., Liew, A., Michos, E. D., Olowu, W. A., Sadusky, T., et al. (2020) · Annals of Internal Medicine · doi:10.7326/m20-5938 · Cited by 165 · see citing works
“Continuous glucose monitoring or selfmonitoring of blood glucose may be particularly useful among patients in whom HbA1c is not concordant with directly measured blood glucose levels or clinical symptoms (12).”
BackgroundManaging New-Onset Type 1 Diabetes During the COVID-19 Pandemic: Challenges and Opportunities
Garg, S. K., Rodbard, D., Hirsch, I. B., & Forlenza, G. P. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0161 · Cited by 154 · see citing works
“Hence, the TIR (and, nearly equivalently, the TAR) can be used as the basis for clinical day-to-day management.(27,28,50,51)”
Continuous glucose monitoring targets in type 1 diabetes pregnancy: every 5% time in range matters
Murphy, H. R. (2019) · Diabetologia · doi:10.1007/s00125-019-4904-3 · Cited by 137 · see citing works
Consensus report: Definition and interpretation of remission in type 2 diabetes
Riddle, M. C., Cefalu, W. T., Evans, P. H., Gerstein, H. C., Nauck, M. A., Oh, W. K., Rothberg, A. E., le Roux, C. W., Rubino, F., Schauer, P. R., et al. (2021) · Diabetic Medicine · doi:10.1111/dme.14669 · Cited by 30 · see citing works
Self-citationContinuous glucose monitoring: The achievement of 100 years of innovation in diabetes technology
Galindo, R. J., & Aleppo, G. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108502 · Cited by 138 · see citing works
“[77] Glucose variability is presented as a percentage coefficient of variation (%CV), with the recommended goal of <36%.”
BackgroundSelf-citationTime in range: a new parameter to evaluate blood glucose control in patients with diabetes
Gabbay, M. A. L., Rodacki, M., Calliari, L. E., Vianna, A. G. D., Krakauer, M., Pinto, M. S., Reis, J. S., Puñales, M. K., Miranda, L. G., Ramalho, A. C., et al. (2020) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-020-00529-z · Cited by 129 · see citing works
Encapsulated stem cell–derived β cells exert glucose control in patients with type 1 diabetes
Keymeulen, B., Groot, K. D., Jacobs-Tulleneers-Thevissen, D., Thompson, D. M., Bellin, M., Kroon, E. J., Daniels, M., Wang, R. M., Jaiman, M. S., Kieffer, T., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-02055-5 · Cited by 117 · see citing works
“The glucose management indicator (GMI), a secondary endpoint, expresses mean glycemia as a CGM substitute for HbA1c values that are measured only at 3-month intervals 25,26 .”
Beck, R. W., Kanapka, L., Breton, M. D., Brown, S. A., Wadwa, R., Buckingham, B. A., Kollman, C., & Kovatchev, B. P. (2023) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0558 · Cited by 102 · see citing works
Self-citationEngineering digital biomarkers of interstitial glucose from noninvasive smartwatches
Bent, B., Cho, P. J., Henriquez, M., Wittmann, A., Thacker, C., Feinglos, M. N., Crowley, M. J., & Dunn, J. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00465-w · Cited by 99 · see citing works
Interpreting blood GLUcose data with R package iglu
Broll, S., Urbanek, J., Buchanan, D., Chun, E., Muschelli, J., Punjabi, N., & Gaynanova, I. (2020) · bioRxiv · doi:10.1371/journal.pone.0248560 · Cited by 102 · see citing works
“Our R package iglu calculates all CGM metrics summarized in [2] in addition to several others [6, 12, 19], a full list of currently implemented metrics is summarized in Table 1.”
MethodPeralta, F. G., Choudhary, P., Cosson, E., Irace, C., Rami-Merhar, B. M., & Seibold, A. (2022) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14638 · Cited by 116 · see citing works
“glucose monitoring (CGM) during the last decade has enabled us to track glycaemic control directly, including the metrics of estimated A1c (eA1c) and subsequently the glucose management indicator (GMI).(5) This has also led to a widespread assumption that eA1c or GMI are intended to approximate HbA1c as closely as pos…”
BackgroundMethodInfluentialSherr, J. L., Buckingham, B. A., Forlenza, G. P., Galderisi, A., Ekhlaspour, L., Wadwa, R. P., Carria, L., Hsu, L., Berget, C., Peyser, T. A., et al. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0286 · Cited by 92 · see citing works
“While GMI is generally suggested to be calculated from at least 10 days of sensor use, 27 a recent study indicates that 5 days of sensor data can provide an estimation of mean glucose concentration that is reasonably well-correlated with that calculated from a 3-month period of use.”
Consensus Report: Definition and Interpretation of Remission in Type 2 Diabetes
Riddle, M. C., Cefalu, W. T., Evans, P. H., Gerstein, H. C., Nauck, M. A., Oh, W. K., Rothberg, A. E., le Roux, C. W., Rubino, F., Schauer, P., et al. (2022) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgab585 · Cited by 92 · see citing works
“In some circumstances, an eA1C or GMI <6.5% can be considered an equivalent criterion.”
InfluentialSelf-citationRaj, R., Mishra, R., Jha, N., Joshi, V. R., Correa, R. R., & Kern, P. A. (2022) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2021-002573 · Cited by 110 · see citing works
Johnson, M. L., Martens, T. W., Criego, A. B., Carlson, A. L., Simonson, G. D., & Bergenstal, R. M. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0034 · Cited by 104 · see citing works
Self-citationPotential Clinical Error Arising From Use of HbA1c in Diabetes: Effects of the Glycation Gap
Nayak, A., Singh, B. M., & Dunmore, S. (2019) · Endocrine Reviews · doi:10.1210/er.2018-00284 · Cited by 92 · see citing works
Electrospun nanofiber-based glucose sensors for glucose detection
Du, Y., Zhang, X., Liu, P., Yu, D.-G., & Ge, R.-L. (2022) · Frontiers in Chemistry · doi:10.3389/fchem.2022.944428 · Cited by 87 · see citing works
“are young adults (Bergenstal et al., 2018); 2) Type 2, with a low rate of insulin production or the body is without insulin produced by the pancreas, accounts for 90% of people with”
BackgroundWhole-Grain Processing and Glycemic Control in Type 2 Diabetes: A Randomized Crossover Trial
Åberg, S., Mann, J. I., Neumann, S., Ross, A. B., & Reynolds, A. N. (2020) · Diabetes Care · doi:10.2337/dc20-0263 · Cited by 107 · see citing works
Prahalad, P., Ding, V. Y., Zaharieva, D. P., Addala, A., Johari, R., Scheinker, D., Desai, M., Hood, K., & Maahs, D. M. (2022) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgab859 · Cited by 82 · see citing works
Time in Range from Continuous Glucose Monitoring: A Novel Metric for Glycemic Control
Yoo, J. H., & Kim, J. H. (2020) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2020.0257 · Cited by 95 · see citing works
“The formula was derived using regression analysis of laboratory-measured HbA1c and CGM-measured mean glucose based on population from three clinical trials using specific sensor types (Dexcom G4 and G5; Dexcom, San Diego, CA, USA) in non-Hispanic whites [15-19].”
MethodFlash glucose monitoring reduces glycemic variability and hypoglycemia: real-world data from Spain
Peralta, F. G., Dunn, T. C., Landuyt, K., Xu, Y., & Merino-Torres, J. F. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-001052 · Cited by 99 · see citing works
HbA1c and Glucose Management Indicator Discordance: A Real-World Analysis
Perlman, J. E., Gooley, T., Mcnulty, B., Meyers, J., & Hirsch, I. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0501 · Cited by 80 · see citing works
Dalmazi, G. D., Maltoni, G., Bongiorno, C., Tucci, L., Natale, V. D., Moscatiello, S., Laffi, G., Pession, A., Zucchini, S., & Pagotto, U. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2020-001664 · Cited by 87 · see citing works
cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data
Vigers, T., Chan, C. L., Snell-Bergeon, J., Bjornstad, P., Zeitler, P., Forlenza, G. P., & Pyle, L. (2019) · PLoS ONE · doi:10.1371/journal.pone.0216851 · Cited by 78 · see citing works
“The American Diabetes Association (ADA) recently proposed a set of key metrics for reporting CGM data [8], all of which are calculated by our code, in addition to the glucose management indicator (GMI) [3], time in range [2], and other variables proposed by Hernandez et al. [4].”
BackgroundThe trials and tribulations of determining HbA1c targets for diabetes mellitus
Klein, K. R., & Buse, J. B. (2020) · Nature Reviews Endocrinology · doi:10.1038/s41574-020-00425-6 · Cited by 86 · see citing works
Gilbert, T. R., Noar, A., Blalock, O., & Polonsky, W. H. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0666 · Cited by 84 · see citing works
Evolution of Diabetes Technology
Dovč, K., & Battelino, T. (2019) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2019.10.009 · Cited by 85 · see citing works
Ranjan, A. G., Rosenlund, S. V., Hansen, T. W., Rossing, P., Andersen, S., & Nørgaard, K. (2020) · Diabetes Care · doi:10.2337/dc20-0909 · Cited by 72 · see citing works
Forlenza, G. P., Carlson, A. L., Galindo, R. J., Kruger, D. F., Levy, C. J., McGill, J. B., Umpierrez, G., & Aleppo, G. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0206 · Cited by 70 · see citing works
Self-citationLing, J., Ng, J. K., Chan, J. C. N., & Chow, E. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.869899 · Cited by 80 · see citing works
Real-World Use of a New Hybrid Closed Loop Improves Glycemic Control in Youth with Type 1 Diabetes
Messer, L. H., Berget, C., Pyle, L. L., Vigers, T., Cobry, E. C., Driscoll, K. A., & Forlenza, G. P. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2021.0165 · Cited by 76 · see citing works
Toschi, E., Slyne, C., Sifre, K., O’Donnell, R., Greenberg, J., Atakov-Castillo, A., Carl, S., & Munshi, M. (2020) · Diabetes Care · doi:10.2337/dc20-0016 · Cited by 69 · see citing works
Forlenza, G. P., Buckingham, B. A., Brown, S. A., Bode, B. W., Levy, C. J., Criego, A. B., Wadwa, R. P., Cobry, E. C., Slover, R., Messer, L. H., et al. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0546 · Cited by 78 · see citing works
Standardization process of continuous glucose monitoring: Traceability and performance
Freckmann, G., Nichols, J. H., Hinzmann, R., Klonoff, D. C., Ju, Y., Diem, P., Makris, K., & Slingerland, R. J. (2021) · Clinica Chimica Acta · doi:10.1016/j.cca.2020.12.025 · Cited by 62 · see citing works
Basics and use of continuous glucose monitoring (CGM) in diabetes therapy
Freckmann, G. (2020) · Journal of Laboratory Medicine · doi:10.1515/labmed-2019-0189 · Cited by 66 · see citing works
“The so-called GMI, however, calculates the estimated A1c based on CGM-derived glucose values [38].”
BackgroundExpanding the Role of Continuous Glucose Monitoring in Modern Diabetes Care Beyond Type 1 Disease
Klupa, T., Czupryniak, L., Dzida, G. J., Fichna, P., Jarosz-Chobot, P. A., Gumprecht, J., Myśliwiec, M., Szadkowska, A., Bomba-Opon, D. A., Czajkowski, K., et al. (2023) · Diabetes Therapy · doi:10.1007/s13300-023-01431-3 · Cited by 85 · see citing works
“Therefore, it has been proposed that the glucose management indicator (GMI) [170], a CGM-derived marker, can be used to evaluate overall glucose exposure for people in whom HbA1c may be an unreliable [171, 172].”
BackgroundReal-world evidence of automated insulin delivery system use
Considine, E. G., & Sherr, J. L. (2024) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0442 · Cited by 59 · see citing works
Fabris, C., Heinemann, L., Beck, R., Cobelli, C., & Kovatchev, B. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0236 · Cited by 58 · see citing works
“In 2018, Bergenstal and coworkers introduced a new metric for converting CGMderived mean BG to HbA1c units, which was named ‘‘glucose management indicator’’ (GMI).(27,28) A decision was made to abandon the term ‘‘estimated A1c’’ (eA1c) to avoid confusion if eA1c and laboratory HbA1c did not agree.”
BackgroundSelf-citationDigital Technology for Diabetes
Hughes, M. S., Addala, A., & Buckingham, B. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2215899 · Cited by 59 · see citing works
Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance
Abuin, P., Rivadeneira, P. S., Ferramosca, A., & González, A. H. (2020) · Journal of Process Control · doi:10.1016/j.jprocont.2020.06.009 · Cited by 52 · see citing works
Wu, Z.-K., Luo, S.-H., Zheng, X.-Y., Bi, Y., Xu, W., Yan, J.-H., Yang, D.-Z., & Weng, J. (2020) · Therapeutic Advances in Endocrinology and Metabolism · doi:10.1177/2042018820950146 · Cited by 57 · see citing works
Associations between HbA1c and continuous glucose monitoring‐derived glycaemic variables
Hirsch, I. B., Welsh, J. B., Calhoun, P. M., Puhr, S., Walker, T. C., & Price, D. A. (2019) · Diabetic Medicine · doi:10.1111/dme.14065 · Cited by 63 · see citing works
Reference Guide for Integrating Continuous Glucose Monitoring Into Clinical Practice
Kruger, D. F., Edelman, S. V., Hinnen, D. A., & Parkin, C. G. (2019) · The Diabetes Educator · doi:10.1177/0145721718818066 · Cited by 55 · see citing works
Zelnick, L. R., Batacchi, Z. O., Ahmad, I., Dighe, A., Little, R. R., Trence, D. L., Hirsch, I. B., & de Boer, I. H. (2020) · Diabetes Care · doi:10.2337/dc20-0915 · Cited by 55 · see citing works
Aleppo, G., & Webb, K. M. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818813581 · Cited by 54 · see citing works
“Glucose average or glucose exposure to have a sense of the glucose management indicator (GMI).(60) 4.”
Self-citationTime in range centered diabetes care
Dovč, K., & Battelino, T. (2021) · Clinical Pediatric Endocrinology · doi:10.1297/cpe.30.1 · Cited by 64 · see citing works
“The consensus suggested easy to understand TIR targets, along with TBR and TAR targets for routine management of type 1 and type 2 diabetes (Table 1).”
MethodInfluentialAsgharzadeh, A., Patel, M., Connock, M. J., Damery, S. L., Ghosh, I., Jordan, M., Freeman, K., Brown, A., Court, R. A., Baldwin, S., et al. (2024) · Health Technology Assessment · doi:10.3310/jypl3536 · Cited by 32 · see citing works
Self-citationStandardizing the haemoglobin glycation index
Hempe, J., Yang, S.-P., Liu, S.-Q., & Hsia, D. (2021) · Endocrinology, Diabetes & Metabolism · doi:10.1002/edm2.299 · Cited by 51 · see citing works
Type 1 diabetes and COVID-19: The “lockdown effect”
Aragona, M., Rodia, C., Bertolotto, A., Campi, F., Coppelli, A., Giannarelli, R., Bianchi, C., Dardano, A., & Prato, S. D. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108468 · Cited by 50 · see citing works
“To the purpose of this analysis, we analyzed Average Glucose, Glucose Management Indicator (GMI), Glucose Variability (calculated as the coefficient of variation, CV), Time In Range (TIR, 70–180 mg/dl), Time Above Range (TAR, >180 mg/dl) and Time Below Range (TBR, <70 mg/ dl) [9,10] across 4 lockdown periods: 14 days…”
BackgroundMethodPrahalad, P., Zaharieva, D. P., Addala, A., New, C., Scheinker, D., Desai, M., Hood, K., & Maahs, D. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.00360 · Cited by 50 · see citing works
Kovatchev, B. P., Singh, H., Mueller, L., & Gonder-Frederick, L. A. (2022) · Diabetes Care · doi:10.2337/dc22-1217 · Cited by 49 · see citing works
“Note that the GMI is a unit-less linear function of average glucose, presented sometimes as a percentage because of its clinical analogy with HbA1c (24).”
BackgroundMethodInfluentialLu, J., Ma, X., Zhang, L., Mo, Y., Lu, W., Zhu, W., Bao, Y., Jia, W., & Zhou, J. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108032 · Cited by 49 · see citing works
Tauschmann, M., Cardona‐Hernandez, R., DeSalvo, D. J., Hood, K. K., Лаптев, Д. Н., Olinder, A. L., Wheeler, B. J., & Smart, C. E. (2025) · Hormone Research in Paediatrics · doi:10.1159/000543156 · Cited by 67 · see citing works
“…70 and 180 mg/dL or 3.9 and 10 mmol/L), glucose management indicator (GMI) (estimated HbA1c value derived from CGM data over a de fi ned duration) [26], or most recently “ time in tight range ” [27] (the time spent within the 70 – 140 mg/ dL or 3.9 – 7.8 mmol/L range), have emerged as valuable…”
InfluentialHengist, A., Davies, R. G., Walhin, J., Buniam, J., Merrell, L. H., Rogers, L. M., Bradshaw, L., Moreno‐Cabañas, A., Rogers, P. J., Brunstrom, J. M., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101667 · Cited by 53 · see citing works
Real‐world performance of the MiniMed™ 670G system in Europe
da Silva, J., Bosi, E., Jendle, J., Arrieta, A., Castañeda, J., Grossman, B., Cordero, T., Shin, J. J., & Cohen, O. (2021) · Diabetes, obesity and metabolism · doi:10.1111/dom.14424 · Cited by 47 · see citing works
“International and national guidelines have, accordingly, been updated to the near-physiological recommended goals of HbA1c less than 53 mmol/mol ( 7.0%), indicating a preference for lower HbA1c levels such as 48 mmol/mol (6.5%), provided it can be achieved safely.1,2 Additionally, professional societies have endorsed…”
BackgroundInfluentialZelnick, L. R., Batacchi, Z., Ahmad, I., Dighe, A. L., Little, R., Trence, D., Hirsch, I., & de Boer, I. D. (2020) · Diabetes Care · doi:10.2337/figshare.12674651.v1 · Cited by 47
Information and consensus document for the detection and management of chronic kidney disease
Maset, R. G., Bover, J., Segura, J., Diezhandino, M. G., del Hoyo, J. C., Escalada, J., Fácila, L., Gamarra, J., García‐Donaire, J. A., Matarin, L. G., et al. (2022) · Nefrología (English Edition) · doi:10.1016/j.nefroe.2022.07.003 · Cited by 52 · see citing works
Monitoring of Pediatric Type 1 Diabetes
Marks, B. E., & Wolfsdorf, J. I. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.00128 · Cited by 55 · see citing works
Ahmadi, A., Khoshfetrat, S. M., Kabiri, S., Dorraji, P., Larijani, B., & Omidfar, K. (2021) · Microchimica Acta · doi:10.1007/s00604-021-04959-y · Cited by 42
“Therefore, the HbA1c determination in blood sample is important for healthcare providers to make a diagnosis and monitor long-term progress of diabetes without the influence of short-term fluctuation of blood glucose [6].”
BackgroundTime-in-range for monitoring glucose control: Is it time for a change?
Bellido, V., Pinés-Corrales, P. J., Villar‐Taibo, R., & Ampudia‐Blasco, F. J. (2021) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2021.108917 · Cited by 46 · see citing works
Hypoglycemia in People with Type 2 Diabetes and CKD
Ahmad, I., Zelnick, L. R., Batacchi, Z. O., Robinson, N., Dighe, A. L., Manski-Nankervis, J.-A. E., Furler, J., O'Neal, D. N., Little, R. R., Trence, D., et al. (2019) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.11650918 · Cited by 49 · see citing works
“180 mg/dl), coefficient of variation (SD divided by mean of all CGM glucose concentrations, a preferred metric of glycemic variability), SD and interquartile range of all CGM glucose concentrations (alternative variability metrics), and the glucose management indicator (GMI) (a measure of mean blood glucose calculated…”
BackgroundA foundation model for continuous glucose monitoring data
Lutsker, G., Sapir, G., Shilo, S., Merino, J., Godneva, A., Greenfield, J. R., Samocha‐Bonet, D., Dhir, R., Gude, F., Mannor, S., et al. (2026) · Nature · doi:10.1038/s41586-025-09925-9 · Cited by 49 · see citing works
Implications of the Hemoglobin Glycation Index on the Diagnosis of Prediabetes and Diabetes
Hsia, D. S., Rasouli, N., Pittas, A. G., Lary, C. W., Peters, A. L., Lewis, M. R., Kashyap, S. R., Johnson, K., LeBlanc, E. S., Phillips, L. S., et al. (2020) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgaa029 · Cited by 44 · see citing works
Continuous glucose monitoring in cystic fibrosis – A practical guide
Chan, C. L., Ode, K. L., Granados, A., Moheet, A. A., Moran, A., & Hameed, S. A. (2019) · Journal of Cystic Fibrosis · doi:10.1016/j.jcf.2019.08.025 · Cited by 50 · see citing works
Wright, E. E., Morgan, K., Fu, D. K., Wilkins, N., & Guffey, W. J. (2020) · Clinical Diabetes · doi:10.2337/cd20-0042 · Cited by 40 · see citing works
Danne, T. P. A., Joubert, M., Hartvig, N. V., Kaas, A., Knudsen, N. N., & Mader, J. K. (2024) · Diabetes Care · doi:10.2337/dc23-2176 · Cited by 42 · see citing works
Chesser, H., Srinivasan, S., Puckett, C., Gitelman, S. E., & Wong, J. C. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221139873 · Cited by 40 · see citing works
Real-World Data from the First U.S. Commercial Users of an Implantable Continuous Glucose Sensor
Sanchez, P., Ghosh-Dastidar, S., Tweden, K. S., & Kaufman, F. R. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0234 · Cited by 40 · see citing works
“The patient metrics included mean SG, SD, coefficient of variation (CV), and glucose measurement index (GMI; a mathematical estimate of A1c published by Bergenstal et al. 12 ).”
InfluentialPrahalad, P., Scheinker, D., Desai, M., Ding, V. Y., Bishop, F. K., Lee, M. Y., Ferstad, J. O., Zaharieva, D. P., Addala, A., Johari, R., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02975-y · Cited by 45 · see citing works
“The GMI, an established estimator of HbA1c, was computed at 2-week intervals by applying the formula developed by Bergenstal et al. 54 to CGM glucose readings, averaged across a lookback window of up to 90 days.”
Rodbard, D. (2022) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14906 · Cited by 44 · see citing works
“Use of Mean Glucose avoids the uncertainties in the conversion of Mean Glucose into GMI introduced by choice of a reference dataset and the large scatter of the data around the regression line.(3,4,19,20) The increasing stability and”
BackgroundMethodInfluentialGonzalez, A., Deng, Y., Lane, A. N., Benkeser, D. C., Cui, X., Staimez, L. R., Ford, C. N., Khan, F. N., WEBSTER, S. C. M., Leong, A., et al. (2019) · Diabetic Medicine · doi:10.1111/dme.14181 · Cited by 47 · see citing works
Kovatchev, B. P., Lobo, B. J., Fabris, C., Ganji, M., Fathi, A. E., Breton, M. D., Kanapka, L. G., Kollman, C., Battelino, T., & Beck, R. W. (2025) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2024.0404 · Cited by 34 · see citing works
Self-citationKong, A. P. S., Lim, S. K., Yoo, S.-H., Ji, L., Chen, L., Bao, Y., Yeoh, E. C. K., Chan, S.-P., Wang, C., Mohan, V., et al. (2023) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2023.110718 · Cited by 48 · see citing works
Friedman, J. G., Cardona, Z., Szmuilowicz, E. D., & Aleppo, G. (2023) · Pharmacogenomics and Personalized Medicine · doi:10.2147/pgpm.s374663 · Cited by 42 · see citing works
Self-citationKeshet, A., & Segal, E. (2024) · Nature Communications · doi:10.1038/s41467-024-53832-y · Cited by 39 · see citing works
“Measures of hyperglycemia — % above 180, HBGI 31 and Hyper index 32 — showed the lowest numbers of signi fi cant associations with bacterial gene families and pathways. eA1C 33 and GMI 34 , both transformations of the mean-glucosemeasuredfromCGM,showedsimilarassociationpatterns,and were found to be signi fi cantly assoc…”
BackgroundMontaser, E., Fabris, C., & Kovatchev, B. P. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0104 · Cited by 42 · see citing works
“For example, GMI is a linear function of MG, 23 implying a correlation of 1.0, and TIR = 100 - TAR - TBR, by definition.”
InfluentialNew and Emerging Technologies in Type 1 Diabetes
Sherwood, J. S., Russell, S. J., & Putman, M. S. (2020) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2020.07.006 · Cited by 40 · see citing works
Dunn, T. C., Xu, Y., Bergenstal, R. M., Ogawa, W., & Ajjan, R. (2023) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0146 · Cited by 37 · see citing works
“There have been numerous empirical relationships proposed between glucose and HbA1c, both using fasting glucose and average glucose by CGM, 6,11,27,50–52 and the underlying variation is large enough that it is almost certain to find statistically significant differences in the regression terms for each study due to unco…”
InfluentialSelf-citationValenzano, M., Cibrario Bertolotti, I., Valenzano, A., & Grassi, G. (2021) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-001045 · Cited by 34 · see citing works
“Patients were first given instructions about the use of the prescribed devices and the procedures to calibrate their sensors and upload CGM data (in the case they were requested to do so themselves, in particular with rtCGM sensors) during a preliminary visit and a subsequent 2-week training, and…”
- Authors
- Bergenstal, R. M., Beck, R. W., Close, K. L., Grunberger, G., Sacks, D. B., Kowalski, A., Brown, A. S., Heinemann, L., Aleppo, G., Ryan, D. B., Riddlesworth, T. D., & Cefalu, W. T.
- Affiliations
- Park Nicollet Health Services (US)
- Jaeb Center for Health Research (US)
- The diaTribe Foundation, San Francisco, CA
- Grunberger Diabetes Institute (US)
- National Institutes of Health (US)
- Breakthrough T1D (US)
- Close Concerns (United States) (US)
- Science Consulting in Diabetes, Neuss, Germany
- Northwestern University (US)
- Sacred Heart Hospital (US)
- American Diabetes Association (US)
- Corresponding author
- Bergenstal, Richard M.
- ORCID
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2018-09-17
- Volume
- 41
- Issue
- 11
- Pages
- 2275-2280
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- PMC
- PMC6196826
- OpenAlex
- W2891178783
- Cited by
- 684
- References
- 18
- Citations by year
- 2026: 79 · 2025: 95 · 2024: 114 · 2023: 84 · 2022: 95 · 2021: 101 · 2020: 75 · 2019: 34 · 2018: 6
- FWCI
- 43.18
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Diabetes Care (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Sanofi
- National Institute of Diabetes and Digestive and Kidney Diseases (UC4DK108611)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Keywords
- glucose management indicator, continuous glucose monitoring, HbA1c, mean blood glucose, nomenclatural changes, diabetes management, diabetes education
- MeSH terms
- Blood Glucose, Diabetes Mellitus, Health Education, Health Status Indicators, Glycated Hemoglobin, Humans, Terminology as Topic, Statistics as Topic, Blood Glucose Self-Monitoring
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.
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
