The Whitehorse NoFalls trial: effects on fall rates and injurious fall rates
Bibliographic record
Abstract
BACKGROUND: the burden of falls and fall-related injuries among older adults is well established. Contention surrounds the effectiveness, and hence value, of multi-component fall prevention interventions delivered in the community. OBJECTIVE: using consensus-based analytic guidelines rather than time-to-first fall as the primary endpoint, the objective was to examine the effectiveness of the Whitehorse NoFalls trial on all falls, falls resulting in injury and falls requiring medical care to be sought. DESIGN, SETTING AND PARTICIPANTS: the study was a community-based randomised controlled trial, with 1,090 participants assigned to one of eight groups, these being a combination of one or more of exercise, vision and or home hazard reduction or alternatively assignment to the control group. METHODS: using negative binomial regression, the incidence of all falls, falls resulting in injury and those requiring medical care in the intervention groups were examined. Falls were reported using a monthly return calendar. RESULTS: exercise alone and in combination with vision and/or home hazard reduction was associated with fewer falls. For falls resulting in injury and the subset requiring medical care, the vision plus exercise intervention was associated with fewer falls. CONCLUSIONS: the findings confirm the effectiveness of exercise in preventing falls among community-dwelling older adults and supports contention that multi-component interventions do not prevent more falls than a single intervention. The results highlight the effectiveness of vision plus exercise in preventing more serious falls, a finding which warrants further consideration.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".