Fall risk stratification in older adults: low and not-at-risk status still associated with falls and injuries.
Bibliographic record
Abstract
BACKGROUND: Falls guidelines recommendations for individuals classified as 'not-at-risk' range from no further actions to offering education and exercises. However, there is a scarcity of prospective studies analysing the rate of falls and injuries in this not-at-risk group to inform recommendations. OBJECTIVE: To prospectively estimate the rate of falls and injuries in older adults considered 'not-at-risk' for falls. DESIGN: Prospective cohort study. SETTING: Geriatric Medicine Clinics. SUBJECTS: Community-dwelling older adults aged 65 and older. METHODS: Falls risk stratification was operationalised by adapting the Centers for Disease Control and Prevention's Stopping Elderly Accidents, Deaths and Injuries algorithm. Associations of risk strata (screened not-at-risk vs. at-risk) with incident falls and injuries were estimated using incidence rate ratios [adjusted incident rate ratio (aIRR), Poisson regression model]. Associations between slow gait speed (<1 m/s) and injurious falls were estimated by risk strata using hazard ratios (adjusted hazard ratio, Cox and Poisson regression model). RESULTS: Of 403 participants, 64% of at-risk individuals fell during the follow-up compared to 41.3% in the not-at-risk group, whilst injurious falls were reported by 63.2% of the not-at-risk group and by 59.7% of the at-risk group. At-risk individuals had a higher rate of falls (aIRR = 3.91, 95% CI: 3.30-4.64, P < .001) but a similar rate of injurious falls as the not-at-risk individuals (aIRR = 1.26, 95% CI: 0.93-1.71; P = .11). Not-at-risk individuals with slow gait speed sustained injurious falls at twice the rate (aIRR = 1.83, 95% CI: 1.12-3.91, P = .008) than those without slow gait speed. CONCLUSIONS: Being screened as not-at-risk for falls does not mean no risk at all. Routinely and universally assessing gait speed could identify not-at-risk individuals who are likely to sustain injuries after a fall and could benefit from primary prevention.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".