Efficacy of falls prevention interventions: protocol for a systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Falls are a leading cause of morbidity and mortality in older adults. Although numerous trials of falls prevention interventions have been completed, there is extensive variation in their intervention components and clinical context, such that the key elements of an effective falls prevention program remain unclear to patients, clinicians, and policy-makers. Our objective is to identify the most effective interventions and combinations of interventions that prevent falls though a systematic review and meta-analysis, including a network meta-analysis. METHODS/DESIGN: We will search for published (e.g., MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Ageline) and unpublished (e.g., trial registries, dissertations) randomised clinical trials (RCTs) in all languages examining interventions to prevent falls compared to usual care or other falls prevention interventions among adults aged ≥65 years from all settings (e.g., community, acute care, long-term care, and rehabilitation). The primary outcomes are number of injurious falls and number of hospitalizations due to falls. Secondary outcomes include falls rate, number of fallers, number of emergency room visits due to falls, number of physician visits due to falls, number of fractures, costs, and number of intervention-related harms (e.g., muscle soreness related to exercise).We will calibrate our eligibility criteria amongst the team and two independent team members will screen the literature search results in duplicate. Conflicts will be resolved through team discussion. A similar process will be used for data abstraction and quality appraisal with the Cochrane risk of bias tool.Our results will be synthesized descriptively and a random effects meta-analysis will be conducted if the studies are deemed methodologically, clinically, and statistically (e.g., I²<60%) similar. If appropriate, a network meta-analysis will be conducted, which will allow the comparison of interventions that have not been compared in head-to-head RCTs, as well as the effectiveness of interventions. DISCUSSION: We will identify the most effective interventions and combinations of interventions that prevent falls in older people. Our results will be used to optimize falls prevention strategies, and our goal is to ultimately improve the health of seniors internationally. TRIAL REGISTRATION: PROSPERO registry number: CRD42013004151.
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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 | 0.081 | 0.132 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.025 | 0.038 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.083 | 0.008 |
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".