Bibliographic record
Abstract
Current type 2 diabetes therapies are mainly targeted at stimulating pancreatic beta-cell secretion and reducing insulin resistance. A number of alternative therapies are currently being developed to take advantage of the actions of the incretin hormones Glucagon-Like Peptide-1 (GLP-1) and Glucose-dependent Insulinotropic Polypeptide (GIP). These hormones are released from the small intestine in response to nutrient ingestion and stimulate insulin secretion in a glucose-dependent manner. One approach to potentiating their actions is based on inhibiting dipeptidyl peptidase IV (DPP IV), the major enzyme responsible for degrading the incretins in vivo. DPP IV exhibits characteristics that have allowed the development of specific orally administered inhibitors with proven efficacy in improving glucose tolerance in animal models of diabetes. A number of clinical trials have demonstrated that DPP IV inhibitors are effective in improving glucose disposal and reducing hemoglobin A1c levels in type 2 diabetic patients and one inhibitor, sitagliptin, is now in therapeutic use, with others likely to receive FDA approval in the near future. Studies aimed at elucidating the mode of action of the inhibitors are still ongoing. Both enhancement of insulin secretion and reduction in glucagon secretion, resulting from the blockade of incretin degradation, are believed to play important roles in DPP IV inhibitor action. Preclinical studies indicate that increased levels of incretins improve beta-cell secretory function and exert effects on beta-cell mitogenesis and survival that can preserve beta-cell mass. Roles for other hormones, neuropeptides and cytokines in DPP IV inhibitor-medicated responses are also possible.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".