Functional Performance as a Predictor of Injurious Falls in Older Adults
Bibliographic record
Abstract
OBJECTIVES: To determine whether a performance battery and its components aid in predicting injurious falls. DESIGN: Longitudinal analysis; prospective cohort study. SETTING: Clinical site. PARTICIPANTS: Boston-area community-dwelling adults (N = 755; mean age ± SD 78.1 ± 5.4, 64.1% female, 77.6% white). MEASUREMENTS: Baseline functional performance was determined according to the Short Physical Performance Battery (SPPB), measuring balance, gait speed, and five repeated chair stands. Fall history (past year) and efficacy in performing 10 daily activities without falling were assessed. Falls were assessed using a daily calendar over 4 years. Injurious falls were defined as resulting in fractures, sprains, dislocations, pulled or torn muscles, ligaments, or tendons or seeking medical attention. RESULTS: Poorest chair stand performance (≥16.7 seconds) was associated with greater hazard of injurious falls than in all other chair stand performance groups (hazard ratio (HR) = 1.96, 95% confidence interval (CI) = 1.18-3.26 for ≥ 16.7 vs. 13.7-16.6 seconds; HR = 1.65, 95% CI = 1.07-2.55 for ≥ 16.7 vs. 11.2-13.6 seconds, HR = 1.60, 95% CI = 1.03-2.48 for ≥ 16.7 vs. <11.2 seconds). SPPB did not predict injurious falls. Fall history predicted injurious falls (HR = 1.82, 95% CI = 1.39-2.39); falls efficacy did not. Fall history and a slow chair stand (<16.7 seconds) had a 2-year cumulative incidence rate of an injurious fall of 46% (95% CI = 0.34-0.58), nearly the combined rate of a positive fall history (0.29, 95% CI = 0.25-0.34) and a slow chair stand alone (0.21, 95% CI = 0.13-0.30). CONCLUSION: An easily administered chair stand test may be sufficient for evaluating performance as part of a risk stratification strategy for injurious falls.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".