Prehabilitation before major abdominal surgery: Evaluation of the impact of a perioperative clinical pathway, a pilot study
Bibliographic record
Abstract
Background & objective: Major abdominal surgery morbidity can reach 50%. Prehabilitation has shown promising results in decreasing complications. However, it is unknown if prehabilitation can have a positive effect specifically after major abdominal surgery. The goal of this study was to evaluate the feasibility and safety of a prehabilitation program before major abdominal surgery. Methods: All patients evaluated for major abdominal surgery between February and April 2018 were eligible. A 4-week trimodal prehabilitation program combining physical therapy, nutritional support and psychological preparation was set up. Results: Among 106 patients evaluated for major abdominal surgery during the study period, 60 were included in the prehabilitation program. No cardiovascular events occurred during prehabilitation. The 6-min walking distance increased significantly (+45 m, increase of 9.3%, p = 0.008) after prehabilitation (and before the operation). Anxiety, depression, and several quality of life (QoL) items improved. Postoperative 90-day mortality and morbidity were 3.4% and 48%, respectively. Median hospital length of stay, and intensive care unit length of stay were 14 and 6 days, respectively. For 19 patients readmitted, the treatment was medical, radiological, or surgical, for 11, 5, and 3 patients, respectively. Conclusions: Prehabilitation before major abdominal surgery is feasible, safe, and improve patients’ functional reserves, QoL, and psychological status.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".