The impact of prehabilitation on outcomes in frail and high-risk patients undergoing major abdominal surgery: A systematic review and meta-analysis
Bibliographic record
Abstract
Background & aims: Prehabilitation comprises multidisciplinary preoperative interventions including exercise, nutritional optimisation and psychological preparation aimed at improving surgical outcomes.The aim of this systematic review and meta-analysis was to determine the impact of prehabilitation on postoperative outcomes in frail and high-risk patients undergoing major abdominal surgery.Methods: Embase, Medline, CINAHAL and Cochrane databases were searched from January 2010 to January 2023 for randomised clinical trials (RCTs) and observational studies evaluating unimodal (exercise) or multimodal prehabilitation programmes.Meta-analysis was limited to length of stay (primary end point), severe postoperative complications (Clavien-Dindo Classification !Grade 3) and the 6minute walk test (6MWT).The analysis was performed using RevMan v5.4 software.Results: Sixteen studies (6 RCTs, 10 observational) reporting on 3339 patients (1468 prehabilitation group, 1871 control group) were included.The median (interquartile range) age was 74.0 (71.0 e78.4) years.Multimodal prehabilitation was applied in fifteen studies and unimodal in one.Metaanalysis of nine studies showed a reduction in hospital length of stay (weighted mean difference À1.07 days, 95 % CI À1.60 to À0.53 days, P < 0.0001, I 2 ¼ 19 %).Ten studies addressed severe complications and a meta-analysis suggested a decline in occurrence by up to 44 % (odds ratio 0.56, 95 % CI 0.37 to 0.82, P < 0.004, I 2 ¼ 51 %).Four studies provided data on preoperative 6MWT.The pooled weighted mean difference was 40.1 m (95 % CI 32.7 to 47.6 m, P < 0.00001, I 2 ¼ 24 %), favouring prehabilitation.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".