Short‐term intensified insulin treatment in type 2 diabetes: long‐term effects on β‐cell function
Bibliographic record
Abstract
The natural history of type 2 diabetes (T2DM) is characterized by progressive deterioration of pancreatic β-cell function, leading to worsening glycemia over time. As current antidiabetic therapies have not yet been shown to profoundly alter this natural history, many patients ultimately will require exogenous insulin therapy to obtain adequate glycemic control. Interestingly, the temporary use of short-term intensive insulin therapy early in the course of T2DM has recently emerged as a therapeutic option that may offer favourable long-term effects on β-cell function. Indeed, after receiving this treatment, many patients will experience sustained euglycemia without requiring any antidiabetic therapy. This apparent 'remission' of diabetes is likely secondary to improved β-cell function and can last for more than a year, although it is not sustained and hyperglycemia eventually will return. Nevertheless, owing to its effects on β-cell function, short-term intensive insulin therapy holds promise as a means for modifying the natural history of T2DM and warrants further study in this context. In this report, we will review the rationale and evidence underlying this interesting therapeutic option, and its implications for both clinical research and the management of patients with T2DM.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".