Bibliographic record
Abstract
Saxagliptin is the latest addition to incretin-based therapies in the management of type 2 diabetes. The oral selective dipeptidyl peptidase 4 (DPP-4) inhibitor is just the second in its class to be approved in the U.S. It has been designed to exert long-lasting yet reversible inhibition of the DPP-4 enzyme, thereby slowing down the inactivation of the incretin hormones and enhancing the incretin effect. Saxagliptin is approved in the U.S. as an adjunct therapy to diet and exercise to improve glycemic control. In Canada and Europe, saxagliptin is approved as add-on therapy to one of long-existing antihyperglycemic agents when these drugs, together with diet and exercise, do not adequately achieve glycemic goals. Published clinical trial data indicate that saxagliptin as monotherapy or add-on therapy to metformin, sulfonylureas and thiazolidinediones is effective in improving glycemic control (as measured by hemoglobin A1(C) [HbA1(C)] levels) and achieving glycemic targets (<7% HbA1(C)). It has also been shown to be well-tolerated, with the additional advantage of not having any clinically relevant effect on weight and hypoglycemia incidence. These added benefits are expected to improve therapy adherence.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".