The quality and applicability of clinical practice guidelines for falls prevention, assessment and management across the lifespan: A systematic review
Bibliographic record
Abstract
Background: The burden of falls across the lifespan is substantial. Given the variety of clinical practice guidelines available for falls management, it is useful to understand the quality and clinical applicability of guidelines and their recommendations. Objective: To assess the quality and applicability of clinical practice guidelines for falls prevention, assessment and management across the lifespan. Design: Systematic review, reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data Sources: CINAHL, Cochrane, Embase, MEDLINE, PsycINFO and grey literature databases were searched from January 2016 to April 2025. Methods: Two investigators assessed the methodological quality of the guidelines and recommendations based on the eligibility criteria established a priori. Scaled domain scores were calculated for AGREE II and AGREE-REX. Results: Eleven guidelines met the eligibility criteria and were included for analysis. Most provided recommendations for older adults with only two guidelines focused on adults. AGREE II scores ranged from 32 % to 83 %, while AGREE-REX scores ranged from 39 % to 56 %. Three guidelines were of high quality (≥70 %): (1) 2017 Registered Nurses' Association of Ontario, (2) 2025 National Institute for Health and Care Excellence, and (3) 2022 World Falls Guideline. Conclusion: This systematic review identified gaps in evidence-based guidelines for falls prevention, particularly for individuals under 65. Variations in guideline quality suggest the need for refined development processes, emphasizing stakeholder involvement and applicability. Despite limitations in the current evidence, this review offers a foundation for future guideline development, highlighting the importance of developing inclusive, evidence-based guidelines to address falls for children and adults under the age of 65. Registration: The review protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO registration CRD42023446557). Tweetable Abstract: Gaps in fall prevention, assessment, and management guidelines emphasize the need for evidence-based recommendations for children and adults under the age of 65.
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 | 0.103 | 0.412 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".