256-OR: Costarting Sitagliptin with Metformin Is Associated with a Lower Likelihood of Disease Progression in Newly Treated Patients with Type 2 Diabetes
Bibliographic record
Abstract
Aim: Sitagliptin can potentially preserve beta cell function by promoting local action of GLP-1 in the pancreas. This study examined if early addition of sitagliptin to metformin is associated with a delay in the progression of type 2 diabetes. Methods: Administrative health records from Alberta, Canada between April 1, 2008 and March 31, 2015 were used to conduct a retrospective cohort study. New metformin users were identified and included if they added sitagliptin during follow-up. Patients costarting therapy with sitagliptin and metformin were compared to those who added sitagliptin after initial metformin therapy. Potential confounding variables included age, sex, comorbidities, and cardiovascular medications at baseline. A multivariable logistic regression model was used to evaluate the association between sitagliptin addition (costart versus delayed) and insulin initiation. Change in A1c 1 year after adding sitagliptin was evaluated using a multivariable linear regression model. Results: Mean (SD) age of the 8,746 included patients was 52.1 (11.1) years, 5,655 (64.7%) were men, and 1,149 (13.1%) costarted treatment with metformin and sitagliptin. Insulin was added to the therapy of 192 (16.7%) costarters and 1,640 (21.6%) of delayed sitagliptin users (adjusted odds ratio 0.73; 95% CI: 0.62 to 0.86). The A1c change 1 year after starting sitagliptin was greater in the costarters (-2.1%; SD 2.8%) compared to delayed sitagliptin users (-1.0%; SD 1.9%). After adjusting for baseline A1c and other covariables, costarters had a greater reduction in A1c -0.56% (95% CI: -0.74% to -0.38%). Conclusions: Costarting sitagliptin with metformin is associated with a lower likelihood of disease progression in newly treated patients with type 2 diabetes compared with adding sitagliptin later in therapy. Disclosure S.A. Campbell: None. P.E. Light: None. S.H. Simpson: None. Funding Canadian Institutes of Health Research
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".