Polypharmacy and risk of fractures in older adults: A systematic review
Bibliographic record
Abstract
BACKGROUND: Fractures have serious health consequences in older adults. While some medications are individually associated with increased risk of falls and fractures, it is not clear if this holds true for the use of many medications (polypharmacy). We aimed to identify what is known about the association between polypharmacy and the risk of fractures in adults aged ≥65 and to examine the methods used to study this association. METHODS: We conducted a systematic review with narrative synthesis of studies published up to October 2023 in PubMed, Embase, CINAHL, PsychINFO, Cochrane Library, Web of Science, and the grey literature. Two independent reviewers screened titles, abstracts, and full texts, then performed data extraction and quality assessment. RESULTS: Among the 31 studies included, 11 different definitions of polypharmacy were used and were based on three medication counting methods (concurrent use 15/31, cumulative use over a period 6/31, daily average 3/31, and indeterminate 7/31). Overall, polypharmacy was frequent and associated with higher fracture risk. A dose-response relationship between increasing number of medications and increased risk of fractures was observed. However, only seven studies adjusted for major confounders (age, sex, and chronic disease). The quality of the studies ranged from poor to high. CONCLUSIONS: Polypharmacy appears to be a relevant modifiable risk factor for fractures in older individuals that can easily be used to identify those at risk. The diversity of medication calculation methods and definitions of polypharmacy highlights the importance of a detailed methodology to understand and compare results.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".