EFFECT OF A HOME-BASED EXERCISE PROGRAM ON SUBSEQUENT FALLS IN SENIORS AFTER A FALL: A RANDOMIZED CLINICAL TRIAL
Bibliographic record
Abstract
Abstract We assessed the efficacy of the home-based Otago Exercise Program (OEP) as a secondary falls prevention strategy in seniors referred to a falls prevention clinic after an index fall. We conducted a 12-month randomized controlled trial of 344 adults, aged 70 years and older, with = or > 1 fall resulting in medical attention in the prior 12 months. Participants were randomized to OEP or standard of care (CON). The OEP is a home-based strength and balance training program delivered by a physical therapist. All participants received AGS Guideline Care for falls prevention from a geriatrician. Differences in falls rate was tested with a negative binomial regression model. The rate of falls was lower in the OEP group vs the CON group (incident rate ratio [IRR] = 0.64, 95% CI 0.46 to 0.90). The estimated incidence rate of falls per person-year was 1.4 (95% CI 0.1 to 2.0) in the OEP group and 2.1 (95% CI 0.1 to 3.2) in the CON group, with an absolute incidence rate difference of 0.74 (95% CI 0.04 to 1.78) falls per person-year. DSST performance also increased in the OEP group by a mean change of 1.1 points (95% CI 0.02 to 2.1) vs the CON group. Improved DSST was associated with fewer falls (IRR = 0.80, 95% CI 0.68 to 0.95). These findings support the use of the OEP for secondary falls 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".