Effect of Anticholinergic Medications on Falls, Fracture Risk, and Bone Mineral Density Over a 10-Year Period
Bibliographic record
Abstract
BACKGROUND: Many medications used in older adults have strong anticholinergic (ACH) properties, which may increase the risk of falls and fractures. Use of these medications was identified in a population-based Canadian cohort. OBJECTIVE: To identify the fall and fracture risk associated with ACH medication use. METHODS: Data collection and analysis were conducted at baseline, year 5, and year 10. Cross-sectional analyses were performed to examine associations between ACH medication use and falls. Time-dependent Cox regression was used to examine time to first nontraumatic fracture. Finally, change in bone mineral density (BMD) over 10 years was compared in ACH medication users versus nonusers. RESULTS: Strongly ACH medications were used by 618 of 7753 participants (8.0%) at study baseline, 592 (9.5%) at year 5, and 334 (7.7%) at year 10. Unadjusted ACH medication use was associated with falls at baseline (odds ratio = 1.50; 95% CI = 1.14-1.98; P = 0.004), but the association was no longer significant after covariate adjustment. Similar results occurred at years 5 and 10. ACH medication use was associated with increased incident fracture risk before (hazard ratio = 1.22; CI = 1.13-1.32; P < 0.001) but not after covariate adjustment. Mean (SD) change in femoral neck BMD T-score over 10 years, in those using ACH medications at both years 0 and 5, was -0.60 (0.63) in ACH users versus -0.49 (0.45) in nonusers (P = 0.041), but this was not significant after covariate adjustment. CONCLUSIONS: ACH medications were not found to be independently associated with an increased risk of falling, fractures, or BMD loss. Rather, factors associated with ACH medication use explained the apparent associations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".