Comprehensive Geriatric Care to Improve Mobility after Hip Fracture: An RCT
Bibliographic record
Abstract
BACKGROUND: Comprehensive geriatric care (CGC) for older adults during hospitalization for hip fracture can improve mobility, but it is unclear whether CGC delivered after a return to community living improves mobility compared with usual post-discharge care. OBJECTIVE: To determine if an outpatient clinic-based CGC regime in the first year after hip fracture improved mobility performance at 12 months. METHODS: A two-arm, 1:1 parallel group, pragmatic, single-blind, single-center, randomized controlled trial at 3 hospitals in Vancouver, BC, Canada. Participants were community-dwelling adults, aged ≥65 years, with a hip fracture in the previous 3-12 months, who had no dementia and walked ≥10 m before the fracture occurred. Target enrollment was 130 participants. Clinic-based CGC was delivered by a geriatrician, physiotherapist, and occupational therapist. Primary outcome was the Short Physical Performance Battery (SPPB; 0-12) at 12 months. RESULTS: We randomized 53/313 eligible participants with a mean (SD) age of 79.7 (7.9) years to intervention (n = 26) and usual care (UC, n = 27), and 49/53 (92%) completed the study. Mean 12-month (SD) SPPB scores in the intervention and UC groups were 9.08 (3.03) and 8.24 (2.44). The between-group difference was 0.9 (95% CI -0.3 to 2.0, p = 0.13). Adverse events were similar in the 2 groups. CONCLUSION: The small sample size of less than half our recruitment target precludes definitive conclusions about the effect of our intervention. However, our results are consistent with similar studies on this population and intervention.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".