Calcium Channel Blocker–Clarithromycin Drug Interaction Did Not Increase the Risk of Nonvertebral Fracture
Bibliographic record
Abstract
BACKGROUND: Calcium channel blocker (CCB) use in elderly patients lowers blood pressure and can increase the risk of falls and fractures. These drugs are metabolized by the cytochrome P450 3A4 (CYP3A4) enzyme, and blood concentrations of these drugs may rise to harmful levels when CYP3A4 activity is inhibited. Clarithromycin is an inhibitor of CYP3A4, whereas azithromycin is not. OBJECTIVE: In older patients taking a CCB, we investigated whether coprescription of clarithromycin, compared with azithromycin, was associated with a higher risk of fracture. METHODS: This was a population-level retrospective cohort study in Ontario, Canada, from 2003 to 2012 of older adults (mean age = 76 years) newly prescribed clarithromycin (n = 96 226) or azithromycin (n = 94 083) while taking a CCB (amlodipine, nifedipine, felodipine, verapamil, diltiazem). The outcome assessed within 30 days of a new coprescription was a nonvertebral fracture. RESULTS: There were no differences in measured baseline characteristics between the clarithromycin and azithromycin groups. Amlodipine was the most commonly prescribed CCB (more than 50% of patients). Coprescribing clarithromycin, versus azithromycin, was not associated with a higher 30-day risk of nonvertebral fracture (124 patients of 96 226 taking clarithromycin [0.13%] vs 98 patients of 94 083 taking azithromycin [0.10%]; odds ratio = 1.23 [95% CI = 0.94-1.60]; P = 0.134). CONCLUSIONS: Among older adults taking a CCB, concurrent use of clarithromycin, compared with azithromycin, was not associated with a statistically significantly greater 30-day risk of nonvertebral fracture.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".