Combination therapy in type 2 diabetes mellitus: adding linagliptin to a stable regimen of metformin and a sulfonylurea
Bibliographic record
Abstract
Linagliptin, the most recently approved drug of the dipeptidyl peptidase-4 (DPP-4) inhibitor class, is an oral agent used to improve glycemic control in type 2 diabetes mellitus (T2DM). By inhibiting the DPP-4 enzyme, these drugs slow the inactivation of the endogenous incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), in turn reducing blood glucose levels in a glucose-dependent manner. As well as significantly reducing glycosylated hemoglobin, the class has a good safety profile, with a low incidence of hypoglycemia, and is not associated with weight gain. From a practical point of view, they also have simple regimens, generally with once-daily oral administration, and can be used as monotherapy or in combination with other anti-diabetic drugs. Owens and colleagues have reported a 6-month study of linagliptin add-on therapy in patients who were receiving a stable regimen of metformin and a sulfonylurea, but needed additional glycemic control. Linagliptin was associated with significant improvement in glycemic control and was well-tolerated by patients, indicating that it provides a valuable option for a large number of patients with T2DM, especially for those who would prefer to add an oral therapy to a current regimen.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.013 | 0.011 |
| 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".