Dose‐response relationship between sulfonylureas and major adverse cardiovascular events in elderly patients with type 2 diabetes
Bibliographic record
Abstract
PURPOSE: The objective of this study was to determine if there is a dose-response relationship between sulfonylureas and major adverse cardiovascular events (MACE). METHODS: We conducted a retrospective cohort study among elderly patients with no history of acute coronary syndrome or stroke who initiated gliclazide or glyburide therapy between 1998 and 2010. Gliclazide and glyburide users were evaluated separately, and a high-dimensional propensity score (HDPS) was used to match patients initiating therapy with a low or high dose. A time-dependent variable was used to further characterize exposure, which can change during follow-up. Cox proportional hazard regression models were used to compare the risk of MACE between low (reference) and high doses. RESULTS: We identified 14,213 new users of gliclazide or glyburide (mean age, 74.7 (standard deviation 6.4) years; males, 52.8%; and mean follow-up duration, 2.7 (standard deviation 2.9) years). Among gliclazide users, there was a higher risk of MACE with high compared with low dose (crude rates: 32.8 and 28.2 per 1000 person-years, respectively), but this did not reach statistical significance (HDPS-matched hazard ratio) 1.15; 95% confidence interval (0.96-1.38). For glyburide users, however, MACE occurred more frequently in the high compared with low dose (crude rates: 38.9 and 31.5 per 1000 person-years, respectively; HDPS-matched hazard ratio 1.24; 95% confidence interval 1.02-1.50). CONCLUSIONS: Among new users of sulfonylureas, there appears to be a dose-response relationship between glyburide and MACE, but not for gliclazide. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.006 |
| 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 | 0.001 | 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".