Medication Review in Preventing Older Adults’ Fall-Related Injury: a Systematic Review & Meta-Analysis
Bibliographic record
Abstract
Background Medication review is essential in managing adverse drug reactions and improving drug safety in older adults. This systematic review evaluated medication review’s role as a single intervention or combined with other interventions in preventing fall-related injuries in older adults. Methods Electronic databases search was conducted in PubMed, EMBASE, Scopus, and CINAHL. Two reviewers screened titles and abstracts, reviewed full texts, and performed data extraction and risk of bias assessment. Meta-analyses were conducted on studies with similar participants, interventions, outcomes or settings. Results Fourteen randomized, controlled studies were included. The pooled results indicated that medication review as a stand-alone intervention was effective in preventing fall-related injuries in community-dwelling older adults (Risk Difference [RD] = -0.06, 95% CI: [-0.11, -0.00], I2 = 61%, p = .04). Medication review also had a positive impact on decreasing the risk of fall-related fractures (RD = -0.02, 95% CI: [-0.04, -0.01], I2 = 0%, p = .01). Discussion This systematic review and meta-analysis has demonstrated that medication review is effective in preventing fall-related injuries in general, and fractures specifically, in community-dwelling older adults. Future investigations focusing on the process of performing medication review will further inform fall-related injury prevention for older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.021 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".