Leveraging past GMI vs A1c discordance markedly improves A1c prediction in a real-world cohort
Bibliographic record
Abstract
Abstract Objective The glucose management indicator (GMI) is a widely used surrogate for A1c, but clinically significant GMI-A1c discordance is common. Here, we aim to develop a new GMI leveraging past discordance to improve accuracy in A1c prediction. Research Design and Methods This retrospective cohort study included 4,891 A1c records from 2,555 patients seen at BCDiabetes between 2021-Feb and 2025-Sep. Regression models adjusted for past discordance were trained on data from patients enrolled before 2025-Jan-01 and validated on those enrolled afterwards. Clinically significant discordance was defined as an absolute difference between GMI and A1c of ≥0.5%. Results In the validation cohort with 90-day CGM data, 39/256 (15.2%) patients with past discordance available showed clinically significant discordance between BCDiabetes GMI and A1c, compared to 101 (39.4%) patients with discordant Bergenstal GMI (relative risk [RR] 0.39, 95% CI 0.3—0.5, P < 0.0001). For BCDiabetes GMI, mean absolute discordance and Pearson correlation were 0.29% and 0.93, respectively, compared to 0.45% and 0.8 for the Bergenstal GMI. When including all validation patients regardless of availability of past discordance, BCDiabetes GMI showed discordance of 27.9% compared to 37% from Bergenstal GMI (RR 0.76, 0.69—0.83). Using 14-and-28-day CGM data, BCDiabetes GMI again showed reduced discordance compared to Bergenstal GMI with past discordance available (28-day RR: 0.54, 0.44—0.67; 14-day: 0.67, 0.56—0.80). Conclusions BCDiabetes GMI substantially reduces clinically significant discordance, especially when past discordance is available. Graphical abstract Article Highlights Why did we undertake this study? GMI is a widely used surrogate for A1c, but clinically significant GMI-A1c discordance is common. What is the specific question we wanted to answer? Does accounting for past GMI-A1c discordance improve A1c prediction? What did we find? New GMI equations accounting for past GMI-A1c discordance markedly improved A1c prediction. What are the implications of our findings? Discordance-adjusted GMI may improve CGM-based decision-making. Twitter summary This work develops new GMI equations adjusted for past GMI vs A1c discordance, reducing the risk of clinically significant discordance (>0.5%) by over 60% when compared to the traditional GMI.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".