Future Role of Non-Insulin Antihyperglycemic Agents in the Management of Type 1 Diabetes Mellitus
Bibliographic record
Abstract
In contrast to current approaches to Type 2 diabetes (T2DM), the management of Type 1 diabetes (T1DM) continues to be glucocentric. This is understandable considering the substantial lifetime risk of potentially devastating microvascular complications associated with the disease. Consequently, advances in the management of T1DM have largely focused on enhanced insulin preparations, technologies for insulin delivery and blood glucose monitoring. However, despite the use of these therapeutic approaches, only 21% of adults (and fewer children) reach glycemic targets associated with a lower risk of microvascular complications and life expectancy in patients with T1DM is 12 years shorter than that of the general population. Cardiovascular and kidney disease, together with hypoglycemia, are the major causes of mortality in patients with T1DM. Significant morbidity and mortality are associated with T1DM, but also with its treatment. The adverse effects of insulin, causing hypoglycemia (which is often a key barrier to achieving glycemic targets) and body weight gain are well known to clinicians. Insufficient attention has been paid to the burden of diabetes self-management and the negative impact of the disease and its treatment on patients’ quality of life. Should practitioners consider a broader perspective on T1DM management with the objective of reducing microvascular and macrovascular risk, while simultaneously reducing the burden of T1DM and the adverse effects of therapy? Could using non-insulin antihyperglycemic agents (NIAHAs) as adjuncts to insulin assist practitioners in achieving this objective? The potential utility of NIAHAs in the management of T1DM is discussed in this paper.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".