Fall risk question-based tools for fall screening in community-dwelling older adults: a systematic review of the literature
Bibliographic record
Abstract
Fall screening tools aim to accurately identify the high fall risk individuals. To increase ease of administration and cost-effectiveness many studies focus on question-based tools. The purpose of this systematic review was to identify question-based tools for fall risk assessment in community-dwelling older adults over the age of 60 and the risk factors that are covered by these tools. The PRISMA guidelines were followed. A literature search was conducted in PubMed/MEDLINE, Web of Science and Google Scholar. Data quality assessment was performed with the Ottawa-Newcastle scale. The results identified 20 studies that used 22 question-based tools to assess fall risk. The number of questions per tool varied from 1 to 41 questions. Data quality varied greatly, with values 3-9 for cohort and 2-7 for cross-sectional studies. The most commonly reported fall risk factors were fall history, feeling of unsteadiness, fear of falling, muscle strength, gait limitation and incontinence. Healthcare providers should use the above tools with caution regarding the limitations of each tool. Further studies should be designed to address individuals with high fall risk, such as individuals with cognitive impairment, as they are under-represented or excluded from most of the existing studies.
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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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".