Decreased Mortality Associated With the Use of Metformin Compared With Sulfonylurea Monotherapy in Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the relationship between use of metformin and sulfonylurea and mortality in new users of these agents. RESEARCH DESIGN AND METHODS: Saskatchewan Health databases were used to examine population-based mortality rates for new users of oral antidiabetic agents. Individuals with prescriptions for sulfonylurea or metformin in 1991-1996 and no use in the year prior were identified as new users. Prescription records were prospectively followed for 1-9 years; subjects with any insulin use were excluded. Causes of death were identified based on ICD-9 codes in an electronic vital statistics database. Multivariate logistic regression and survival analyses were used to assess the differences in mortality between drug cohorts, after adjusting for potential confounding variables. RESULTS: The total study sample comprised 12,272 new users of oral antidiabetic agents; the average length of follow-up was 5.1 (SD 2.2) years. In subjects with at least 1 year of drug exposure and no insulin use, mortality rates were 750/3,033 (24.7%) for those receiving sulfonylurea monotherapy, 159/1,150 (13.8%) for those receiving metformin monotherapy, and 635/4,683 (13.6%) for those receiving combination therapy over an average 5.1 (SD 2.2) years of follow-up. The adjusted odds ratio (OR) for all-cause mortality for metformin monotherapy was 0.60 (95% CI 0.49-0.74) compared with sulfonylurea monotherapy. Sulfonylurea plus metformin combination therapy was also associated with reduced all-cause mortality (OR 0.66, 95% CI 0.58-0.75). Reduced cardiovascular-related mortality rates were also observed in metformin users compared with sulfonylurea monotherapy users. CONCLUSIONS: Metformin therapy, alone or in combination with sulfonylurea, was associated with reduced all-cause and cardiovascular mortality compared with sulfonylurea monotherapy among new users of these agents.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".