Systematic review of home physiotherapy after hip fracture surgery
Bibliographic record
Abstract
OBJECTIVE: To compare the benefits of home physiotherapy, institution-based physiotherapy and no physiotherapy following hip fracture surgery. DESIGN: Systematic review and meta-analysis of randomized controlled trials. METHODS: Two reviewers independently extracted data from 5 included studies. Standardized mean differences were pooled for health-related quality of life and performance-based outcomes. Review Manager Version 5 was used for data analysis. RESULTS: Analysis of the 5 included studies indicated that home physiotherapy was better than no physiotherapy and similar to outpatient physiotherapy in improving patient-reported health-related quality of life. Performance-based outcomes were marginally better following outpatient physiotherapy compared with home physiotherapy 3 and 6 months after surgery. The risk of bias was high for most outcomes due to methodological issues in the included studies. DISCUSSION: There was a trend of better results with increasing intensity of physiotherapy intervention, but this did not convert into significant effect sizes. The results of this review do not build a strong consensus for recommending one mode of physiotherapy over the others. The quality of evidence was low mainly due to the high risk of bias in the included studies. CONCLUSION: In light of no strong consensus, physiotherapists should continue to follow their current workplace practice policies for determining suitable discharge settings.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".