Costarting sitagliptin with metformin is associated with a lower likelihood of disease progression in newly treated people with type 2 diabetes: a cohort study
Bibliographic record
Abstract
Abstract Aim To examine whether early addition of sitagliptin to metformin is associated with a delay in type 2 diabetes progression. Methods Administrative health records from Alberta, Canada, for the period April 2008 to March 2015, were used to conduct a retrospective cohort study in new metformin users. People who started sitagliptin on the same day they initiated metformin therapy were compared with those who added sitagliptin later. Insulin initiation served as a surrogate marker for diabetes progression, and multivariable logistic regression models were used to evaluate the association with sitagliptin addition (costart vs later use). A mixed‐effects linear regression model was used to examine the effect of timing of sitagliptin addition on HbA1c change over 1 year. Results The mean (sd) age of the 8764 people who used sitagliptin was 52.1 (11.1) years, 5665 (64.6%) were men, and 1153 (13.2%) started sitagliptin on the same day as metformin. Insulin was added to the therapy of 173 (15.0%) costarters and 1453 (19.1%) later sitagliptin users. The adjusted odds ratio for adding insulin was 0.76 (95% CI 0.64 to 0.90) in favour of costarting sitagliptin. HbA1c levels decreased in both groups 1 year after starting sitagliptin, with costarters having a significantly greater reduction [absolute between‐group difference of 0.5% (95% CI 0.3 to 0.7)] compared with later sitagliptin users. Conclusion Costarting drug therapy with sitagliptin and metformin was associated with a lower likelihood of disease progression in people with type 2 diabetes compared with adding sitagliptin later.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".