Optimization of surgical outcomes with prehabilitation
Bibliographic record
Abstract
The concept of preparing surgical candidates with various modalities designed to increase physical, physiological, metabolic, and psychosocial reserves is known as prehabilitation. Prehabilitation has garnered significant attention in recent years as evidence grows describing benefits to clinical and quality of life outcomes. Recent research examining hospital length of stay and readmission rates provides promising findings with respect to the value of prehabilitation in economic and sustainable healthcare models. The role of prehabilitation across the surgical experience exploits common surgical wait-times and the teachable moment that many patients experience upon the identification of a surgical requirement to improve the pre-, peri-, and postoperative experience. Prehabilitation incorporates numerous systemic and regional approaches to conditioning the surgical candidate. These include exercise, nutrition, education, and/or psychosocial approaches that are intended to improve preoperative fitness and preparedness. Importantly, this also promotes and facilitates health behaviour changes not only preoperatively but during the postoperative period and over the long-term. In this paper, we briefly review the historical and current perspectives on prehabilitation and comment on opportunities for greater clinical and empirical understanding in this field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".