A randomized controlled trial of a community-based consultation service to prevent falls.
Bibliographic record
Abstract
BACKGROUND: Multifaceted programs that combine assessment with interventions have been shown to reduce subsequent falls in some clinical trials. We tested this approach to see whether it would be effective if offered as a consultation service using existing health care resources. METHODS: The subjects of this randomized controlled trial had to be aged 65 years or more and had to have fallen within the previous 3 months. They were randomly assigned to receive either usual care or the intervention, which consisted of in-home assessment in conjunction with the development of an individualized treatment plan, including an exercise program for those deemed likely to benefit. The primary outcomes were the proportion of participants who fell and the rate of falling during the following year. Visits to the emergency department and admissions to hospital were secondary outcomes. RESULTS: One hundred and sixty-three subjects were randomly assigned to either the control or the intervention group, and 152 provided data about their falls. There were no significant differences between the control and intervention groups in the cumulative number of falls (311 v. 241, p = 0.34), having one or more falls (79.2% v. 72.0%, p = 0.30) or in the mean number of falls (4.0 v. 3.2, p = 0.43). Analysis of secondary outcomes (health care use) also showed no significant differences between the intervention group and the control group. In the Cox regression analysis, there was no significant difference between the groups in the proportion of subjects having one or more falls (p = 0.55), but there was a significantly (p < 0.001) longer time between falls in the intervention group. In a post hoc subgroup analysis, subjects with more than 2 falls in the 3 months preceding study entry who had been assigned to the intervention group were less likely to fall (p = 0.046) and had a significantly longer time between falls (p < 0.001), when compared with the group who received usual care. INTERPRETATION: The intervention did not decrease significantly the cumulative number of falls, the likelihood of participants having at least one fall over the next year or the mean number of falls. It did increase significantly the time between falls in a survival analysis when age, sex and history of falling were used as covariates.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".