Bibliographic record
Abstract
OBJECTIVE: The goal of this article is to provide a pragmatic approach to implementing a prehabilitation pathway and service guide. DATA SOURCES: The article presents data from peer-reviewed scientific articles (ie, reviews and original studies) and narrative reviews, as well as professional insights and experiences of the author in setting up a prehabilitation clinic. CONCLUSION: Successful setup of a prehabilitation unit is highly feasible and rewarding, if led by enthusiastic idealists who work in cooperation with a passionate multidisciplinary team (MDT) with clear vision to collectively improve patient outcomes and experiences. It requires a well-integrated MDT to deliver a successful prehabilitation service. IMPLICATIONS FOR NURSING PRACTICE: Nurses are the supporting pillar in many areas of the health care system. The field of prehabilitation is no exception to this with the mainstay contribution nursing provides. A combination of patient care, medical knowledge, and administrative capabilities are required to modify the perioperative pathway and introduce the concept of prehabilitation. Nursing staff are ideally positioned to be strong advocates to developing and delivering an effective MDT prehabilitation clinical pathway.
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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 | 0.074 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".