Does prehabilitation before esophagectomy improve postoperative outcomes? A systematic review and meta-analysis
Bibliographic record
Abstract
Esophagectomy for esophageal cancer is associated with high morbidity. It remains unclear whether prehabilitation, a strategy aimed at optimizing patients' physical and mental functioning prior to surgery, improves postoperative outcomes. A systematic review and meta-analysis was conducted to evaluate the effect of prehabilitation on post-operative outcomes after esophagectomy. Data sources included Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, CINAHL, and PEDro, with information from 1 January 2000 to 5 August 2023. The analysis included randomized controlled trials and observational studies that compared prehabilitation interventions to standard care prior to esophagectomy. A random effects model was used to generate a pooled estimate for pairwise meta-analysis, meta-analysis of proportions, and meta-analysis of means. A total of 1803 patients were included with 584 in randomized controlled trials (RCTs) and 1219 in observational studies. In the randomized evidence, there were no significant differences between prehabilitation and control in the odds of postoperative pneumonia (15.0 vs. 18.9%, odds ratio (OR) 1.06 [95% confidence interval (CI): 0.66;1.72]) or pulmonary complications (14 vs. 25.6%, OR 0.68 [95% CI: 0.32;1.45]). In the observational data, there was a reduction in both postoperative pneumonia (22.5 vs. 32.9%, OR 0.48 [95% CI: 0.28;0.83]) and pulmonary complications (26.1 vs. 52.3%, OR 0.35 [95% CI: 0.17;0.75]) with prehabilitation. Hospital and intensive care unit length of stay (days), operative mortality, and severe complications (Clavien-Dindo ≥ 3) did not differ between groups in both the randomized data and observational data. Prehabilitation demonstrated reductions in postoperative pneumonia and pulmonary complications in observational studies, but not RCTs. The overall certainty of these findings is limited by the low quality of the available evidence.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.045 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".