Antihypertensive Therapy and Incidence of Type 2 Diabetes in an Elderly Cohort
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine whether the incidence of type 2 diabetes differed among elderly users of four major antihypertensive drug classes. RESEARCH DESIGN AND METHODS: This was a retrospective, observational cohort study of previously untreated elderly patients (aged > or = 66 years) identified as new users of an antihypertensive drug class between April 1995 and March 2000. Using a Cox proportional hazards model, the primary analysis compared diabetes incidence in users of ACE inhibitors, beta-blockers, and calcium channel blockers (CCBs), with thiazide diuretics allowed as second-line therapy. In the secondary analysis, thiazide diuretics were added as a fourth study group. RESULTS: In the multivariable-adjusted primary analysis (n = 76,176), neither ACE inhibitor use (hazard ratio 0.96 [95% CI 0.84-1.1]) nor beta-blocker use (0.86 [0.74-1.0]) was associated with a statistically significant difference in type 2 diabetes incidence compared with the CCB control group. In the secondary analysis (n = 100,653), compared with CCB users, type 2 diabetes incidence was not significantly different between users of ACE inhibitors (0.97 [0.83-1.1]), beta-blockers (0.84 [0.7-1.0]), or thiazide diuretics (1.0 [0.89-1.2]). CONCLUSIONS: Type 2 diabetes incidence did not significantly differ among users of the major antihypertensive drug classes in this elderly, population-based administrative cohort. These results do not support the theory that different antihypertensive drug classes are relatively more or less likely to cause diabetes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".