The impact of the frequency, duration and type of physiotherapy on discharge after hip fracture surgery: a secondary analysis of UK national linked audit data
Bibliographic record
Abstract
Additional physiotherapy in the first postoperative week was associated with fewer days to discharge after hip fracture surgery. A 7-day physiotherapy service in the first postoperative week should be considered as a new key performance indicator in evaluating the quality of care for patients admitted with a hip fracture. INTRODUCTION: To examine the association between physiotherapy in the first week after hip fracture surgery and discharge from acute hospital. METHODS: We linked data from the UK Physiotherapy Hip Fracture Sprint Audit to hospital records for 5395 patients with hip fracture in May and June 2017. We estimated the association between the number of days patients received physiotherapy in the first postoperative week; its overall duration (< 2 h, ≥ 2 h; 30-min increment) and type (mobilisation alone, mobilisation and exercise) and the cumulative probability of discharge from acute hospital over 30 days, using proportional odds regression adjusted for confounders and the competing risk of death. RESULTS: The crude and adjusted odds ratios of discharge were 1.24 (95% CI 1.19-1.30) and 1.26 (95% CI 1.19-1.33) for an additional day of physiotherapy, 1.34 (95% CI 1.18-1.52) and 1.33 (95% CI 1.12-1.57) for ≥ 2 versus < 2 h physiotherapy, and 1.11 (95% CI 1.08-1.15) and 1.10 (95% CI 1.05-1.15) for an additional 30-min of physiotherapy. Physiotherapy type was not associated with discharge. CONCLUSION: We report an association between physiotherapy and discharge after hip fracture. An average UK hospital admitting 375 patients annually may save 456 bed-days if current provision increased so all patients with hip fracture received physiotherapy on 6-7 days in the first postoperative week. A 7-day physiotherapy service totalling at least 2 h in the first postoperative week may be considered a key performance indicator of acute care quality after hip fracture.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".