Moving the needle on fall prevention: A Geriatric Emergency Care Applied Research (GEAR) Network scoping review and consensus statement
Bibliographic record
Abstract
BACKGROUND: Although falls are common, costly, and often preventable, emergency department (ED)-initiated fall screening and prevention efforts are rare. The Geriatric Emergency Medicine Applied Research Falls core (GEAR-Falls) was created to identify existing research gaps and to prioritize future fall research foci. METHODS: GEAR's 49 transdisciplinary stakeholders included patients, geriatricians, ED physicians, epidemiologists, health services researchers, and nursing scientists. We derived relevant clinical fall ED questions and summarized the applicable research evidence, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews. The highest-priority research foci were identified at the GEAR Consensus Conference. RESULTS: We identified two clinical questions for our review (1) fall prevention interventions (32 studies) and (2) risk stratification and falls care plan (19 studies). For (1) 21 of 32 (66%) of interventions were a falls risk screening assessment and 15 of 21 (71%) of these were combined with an exercise program or physical therapy. For (2) 11 fall screening tools were identified, but none were feasible and sufficiently accurate for ED patients. For both questions, the most frequently reported study outcome was recurrent falls, but various process and patient/clinician-centered outcomes were used. Outcome ascertainment relied on self-reported falls in 18 of 32 (56%) studies for (1) and nine of 19 (47%) studies for (2). CONCLUSION: Harmonizing definitions, research methods, and outcomes is needed for direct comparison of studies. The need to identify ED-appropriate fall risk assessment tools and role of emergency medical services (EMS) personnel persists. Multifactorial interventions, especially involving exercise, are more efficacious in reducing recurrent falls, but more studies are needed to compare appropriate bundle combinations. GEAR prioritizes five research priorities: (1) EMS role in improving fall-related outcomes, (2) identifying optimal ED fall assessment tools, (3) clarifying patient-prioritized fall interventions and outcomes, (4) standardizing uniform fall ascertainment and measured outcomes, and (5) exploring ideal intervention components.
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 | 0.332 | 0.421 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.035 | 0.024 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.011 | 0.027 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".