Ultra-early initiation of postoperative rehabilitation in the post-anaesthesia care unit after major thoracic surgery: case–control study
Bibliographic record
Abstract
BACKGROUND: Physiotherapy is a major cornerstone of enhanced rehabilitation after surgery (ERAS) and reduces the development of atelectasis after thoracic surgery. By initiating physiotherapy in the post-anaesthesia care unit (PACU), the aim was to evaluate whether the ultra-early initiation of rehabilitation (in the first hour following tracheal extubation) would improve the outcomes of patients undergoing elective thoracic surgery. METHODS: A case-control study with a before-and-after design was conducted. From a historical control group, patients were paired at a 3:1 ratio with an intervention group. This group consisted of patients treated with the ultra-early rehabilitation programme after elective thoracic surgery (clear fluids, physiotherapy, and ambulation). The primary outcome was the incidence of postoperative atelectasis and/or pneumonia during the hospital stay. RESULTS: After pairing, 675 patients were allocated to the historical control group and 225 patients to the intervention group. A significant decrease in the incidence of postoperative atelectasis and/or pneumonia was found in the latter (11.4 versus 6.7 per cent respectively; P = 0.042) and remained significant on multivariate analysis (OR 0.53, 95 per cent c.i. 0.26 to 0.98; P = 0.045). A subgroup analysis of the intervention group showed that early ambulation during the PACU stay was associated with a further significant decrease in the incidence of postoperative atelectasis and/or pneumonia (2.2 versus 9.5 per cent; P = 0.012). CONCLUSIONS: Ultra-early rehabilitation in the PACU was associated with a decrease in the incidence of postoperative atelectasis and/or pneumonia after major elective thoracic surgery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".