Managing mobility outcomes in vulnerable seniors ( <i>MMOVeS</i> ): a randomized controlled pilot study
Bibliographic record
Abstract
OBJECTIVE: To estimate feasibility and potential for efficacy of an individualized, exercise-focused, self-management program (i.e. Managing Mobility Outcomes In Vulnerable Seniors ( MMOVeS)), in comparison to exercise information in improving mobility after six months among seniors recently discharged from hospital. DESIGN: Randomized pilot study. SETTING: Two McGill University-teaching hospitals. SUBJECTS: Community dwelling seniors, aged 70 years and older, recently discharged from either participating hospitals. INTERVENTIONS: The physiotherapy-facilitated intervention consisted of (1) evaluation of mobility capacity, (2) setting short- and long-term goals, (3) delineation of an exercise treatment plan, (4) an educational booklet to enhance mobility self-management skills, and (5) six monthly telephone calls. Control group received a booklet with information on exercises targeting mobility limitations in seniors. MAIN MEASURES: Mobility, pain, and health status were assessed at baseline and at six months using multiple indicators drawn from Disabilities of the Arm, Shoulder, and Hand (DASH) Score, Lower Extremity Functional Scale (LEFS) and Short-Form (SF)-36. RESULTS: In all, 26 people were randomized to the intervention (mean age: 81 ± 8; 39% women), and 23 were randomized to the control (mean age: 79 ± 7; 33% women). The odds ratio for the mobility outcomes combined was 3.08 and the 95% confidence interval excluded 1 (1.65-5.77). The odds ratio for pain and health perception favored the MMOVeS group, but the 95% confidence interval included the null value. CONCLUSIONS: This feasibility study highlights the potential for efficacy of an individualized, exercise-focused, self-management program in comparison to exercise information in improving mobility outcome for seniors. Furthermore, a home-program combining self-management skills and exercise taught with minimal supervision prove to be feasible. Finally, data from this study can be used to estimate sample size for a confirmatory trial.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".