Correction: Predicting falls in older adults: an umbrella review of instruments assessing gait, balance, and functional mobility
Bibliographic record
Abstract
Background: To review the validated instruments that assess gait, balance, and functional mobility to predict falls in older adults across different settings.Methods: Umbrella review of narrative-and systematic reviews with or without meta-analyses of all study types.Reviews that focused on older adults in any settings and included validated instruments assessing gait, balance, and functional mobility were included.Medical and allied health professional databases (MEDLINE, PsychINFO, Embase, and Cochrane) were searched from inception to April 2022.Two reviewers undertook title, abstract, and full text screening independently.Review quality was assessed through the Risk of Bias Assessment Tool for Systematic Reviews (ROBIS).Data extraction was completed in duplicate using a standardised spreadsheet and a narrative synthesis presented for each assessment tool.Results: Among 2736 articles initially identified, 31 reviews were included; 11 were meta-analyses.Reviews were primarily of low quality, thus at high risk of potential bias.The most frequently reported assessments were: Timed Up and Go, Berg Balance Scale, gait speed, dual task assessments, single leg stance, functional Reach Test, tandem gait and stance and the chair stand test.Findings on the predictive ability of these tests were inconsistent across the reviews. Conclusions:In conclusion, we found that no single gait, balance or functional mobility assessment in isolation can be used to predict fall risk in older adults with high certainty.Moderate evidence suggests gait speed can be useful in predicting falls and might be included as part of a comprehensive evaluation 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 | 0.008 | 0.105 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.034 | 0.028 |
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".