Preoperative multimodal prehabilitation before elective colorectal cancer surgery in patients with WHO performance status I or II: randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Multimodal prehabilitation is a promising adjunct to the current surgical treatment pathway for colorectal cancer patients to further improve postoperative outcomes, especially for high-risk patients with low functional capacity. The aim of the present study was to test the effect of prehabilitation on immediate postoperative recovery. METHOD: The study was designed as a RCT with two arms (intervention and control). The intervention consisted of 4 weeks of multimodal prehabilitation, with supervised physical training, nutritional support and medical optimization. The control group received standard of care. A total of 40 patients with colorectal cancer (WHO performance status I or II) undergoing elective surgery with curative intent were included. The primary outcome was postoperative recovery within the first 3 postoperative days, measured by Quality of Recovery-15, a validated questionnaire with a scoring range between 0 and 150 and a minimal clinically relevant difference of 8. RESULTS: In total, 36 patients were analysed with 16 in the intervention group and 20 in the control group. The mean age of the included patients was 79 years. The overall treatment effect associated with the intervention was a 21.9 (95% c.i. 4.5-39.3) higher quality of recovery-15 score during the first 3 postoperative days compared to control, well above the minimal clinically relevant difference. CONCLUSION: Four weeks of multimodal prehabilitation prior to elective curative intended colorectal cancer surgery in patients with WHO performance status I or II was associated with a clinically relevant improvement in postoperative recovery.Registration number: NCT04167436 (http://www.clinicaltrials.gov).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".