Effects of antihypertensive drug treatments on fracture outcomes: a meta‐analysis of observational studies
Bibliographic record
Abstract
OBJECTIVE: To quantitatively pool findings from observational studies on the risk of fracture outcomes associated with exposure to five antihypertensive drug classes: angiotensin-converting enzyme (ACE) inhibitors, diuretics (in particular thiazide diuretics), beta-blockers, calcium-channel blockers and alpha-blockers. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Publications listed in the MEDLINE, EMBASE and LILACS databases, the ISI proceedings, and bibliographies of retrieved articles. Sources were searched from the earliest possible dates through December 2005. REVIEW METHODS: We included case-control and cohort studies presenting relative risks and confidence intervals (CIs) for the association between exposure to antihypertensive agents and fracture outcomes. Data were extracted onto a standardized computer worksheet. Study quality was assessed using a 10-point questionnaire specific to case-control or cohort study design. RESULTS: Fifty-four studies were identified. Pooled estimates were computed using the software HEpiMA. The pooled relative risk (RR) of any fracture with use of thiazide diuretics was 0.86 (95% CI 0.81-0.92) and 1.14 (95% CI 0.84-1.54) with use of nonthiazide diuretics. There was a statistically significant reduction of any fracture with use of beta-blockers, (RR 0.86, 95% CI 0.70-0.98). The one study with ACE inhibitor data showed protection (RR 0.81, 95% CI 0.73-0.89). No significant associations were found between fractures and exposure to alpha-blockers or calcium-channel blockers. CONCLUSIONS: Thiazide diuretics and beta-blockers appear to lower the risk of fractures in older adults. However, these agents cannot be recommended as preventive therapies for fractures until data from randomized controlled trials have established their efficacy. Patients who use these inexpensive drugs as treatments for hypertension may also benefit from a reduction in fracture risk.
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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 | 0.032 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.053 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".