Contemporary Clinical Perspectives on Targeting Remission of Type 2 Diabetes
Bibliographic record
Abstract
It has long been known that some patients with type 2 diabetes (T2DM) can experience sustained metabolic improvement to near-normal levels of glycemia either spontaneously or after medical intervention. Now recognized as remission of diabetes, this intriguing state is currently more feasible than ever before due to profound advances in metabolic surgery, pharmacologic therapy, and regimens of lifestyle modification. This enhanced capacity to induce remission has revealed new pathophysiologic insights, including the presence of a reversible component of the pancreatic beta-cell dysfunction that otherwise drives the chronic progressive nature of T2DM. In doing so, it has changed the therapeutic landscape by offering new potential management objectives and considerations for patients and providers. However, the excitement around these developments must also be tempered by the sobering realities of our current understanding of remission, including the recognition that this condition may not be permanent (resulting in glycemic relapse over time) and that beta-cell function may not be normalized in the setting of remission. These limitations highlight both the many gaps in our current understanding of remission and the caution with which clinical discussions must be handled for clear patient-directed communication of the pros and cons of targeting this outcome in practice. In this mini-review, we consider this rapidly growing literature, including its implications and its limitations, and thereby seek to provide objective balanced perspectives on targeting remission of T2DM in current clinical care.
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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 | 0.006 |
| 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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".