MEDICATION REVIEW IN PREVENTING FALL-RELATED INJURIES IN OLDER ADULTS-A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Medication use is considered a modifiable risk factor for falls and fall-related injuries in older adults. Hence, medication review is essential in managing adverse drug reactions, facilitating appropriate prescribing and improving drug safety in older adults. This systematic review summarized information on the effectiveness of medication review as a single intervention or in combination with other interventions in preventing fall-related injuries in older adults. Electronic databases search was conducted in PubMed, EMBASE, Scopus and CINAHL. Two reviewers screened titles and abstracts, reviewed full-texts, performed data extraction and risk of bias assessment. Meta-analyses were conducted on studies with similar participants, interventions, outcomes or settings. Fourteen randomized, controlled studies were included in this review. Seven studies were of low risk of bias. Four studies evaluated medication review as a single intervention. The pooled result 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 to -0.00), I2=61%, P=0.04). Meta-analysis conducted on five studies with fractures due to falls as the outcomes also showed that medication review had a positive impact on decreasing the risk of fall-related fractures (RD= -0.02, 95%CI (-0.04 to -0.01), I2=0%, P=0.01). 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 details of performing medication review will further inform improving the health of older adults.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".