Bibliographic record
Abstract
Background: Postoperative morbidity is largely the product of the preoperative condition of the patient, the quality of surgical care provided, and the degree of surgical stress elicited. Enhanced Recovery After Surgery (ERAS) minimizes surgical stress with standardized evidence-based perioperative care; yet the ERAS care elements focus mainly on the intra- and postoperative periods, which may not sufficiently enhance recovery if preoperative patient-related factors have not been modified before surgery. Prehabilitation programs aim to enhance recovery by targeting the preoperative condition of the patient.Methods: This dissertation includes four manuscripts that broadly contribute to the evidence that supports the hypothesis that the patient’s preoperative status modifies outcomes in colorectal surgery. Results: First, intermediately frail and frail patients with poor functional walking capacity before surgery suffer more postoperative complications than patients with better functional walking capacity. Second, nutrition prehabilitation, with and without exercise, reduces mean length of hospital stay by two days. Third, patient interviews suggest that patients support the idea of using prehabilitation to enhance their preoperative condition. Finally, the last manuscript offers methodological suggestions to measure and analyze external variables as a means of advancing the prehabilitation literature and further enhancing patient outcomes. Conclusion: The findings of this doctoral dissertation add to the growing body of evidence that the process of surgical recovery begins before surgery. Prehabilitation interventions can be applied to support better postoperative recoveries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".