Should A1C Targets Be Individualized for All People With Diabetes?
Bibliographic record
Abstract
Diabetes guidelines and organizations typically advocate a target glycated hemoglobin (A1C) value of 6.5–7.0% but highlight that glycemic management must be individualized. Whereas individualization of both glycemic targets and management is appealing to the clinician as a way of potentially maximizing benefit while minimizing risk, there is little evidence that such an approach will bring more patients to target. It may be argued that this approach could contribute to fewer patients attaining optimal glycemic targets. Nonetheless, the results of recent large outcome trials clearly highlight the fact that individual glycemic target achievement varied markedly, with some patients apparently deriving more clinical benefit and others deriving more harm. At the same time, there is ongoing evidence of a treatment gap in many surveys of clinical practice and a suggestion that algorithm-driven protocols may be more effective. Collectively, therefore, the currently available evidence suggests that algorithm-driven protocols that incorporate individualized targets based on patient characteristics designed to preserve a sound balance between the benefits and risk of good glycemic control may be an appropriate way of getting more patients to target in a safe and effective manner. Over 280 million people worldwide are known to have diabetes (1), and this number is projected to grow to 438 million by 2030 (2). Current diabetes treatment guidelines (3–9) encourage a multifaceted therapeutic approach (10,11). Central to these recommendations is early diagnosis and active intervention to realize and maintain glycemic control, with the aim of stopping the development of microvascular complications, reducing the risk of macrovascular events, and ameliorating the symptoms of acute hyperglycemia (7,10–17). The prognostic significance of A1C in regard to the incidence of diabetes complications, and the risk reductions associated with improvements in A1C, have been documented in both type 1 (12, …
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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 | 0.021 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".