Prehabilitation: preoperative rehabilitation interventions for lung cancer – a scoping review
Bibliographic record
Abstract
Background: Individuals undergoing lung cancer surgery often face significant postoperative challenges, underscoring the importance of identifying effective preoperative rehabilitation strategies to support recovery. Aim: To identify rehabilitation interventions that can be implemented during the preoperative period for individuals with lung cancer undergoing thoracic surgery. Design: Scoping review guided by the Arksey and O'Malley methodological framework. Methods: The research question guiding this review was "What rehabilitation interventions should be implemented in the preoperative period for individuals with lung cancer undergoing surgery?" A comprehensive search was performed across five databases: MEDLINE, Cochrane Central, CINAHL, ScienceDirect, and PubMed. The review included studies that addressed rehabilitation interventions before thoracic surgery for individuals with lung cancer. Results: A total of 19 articles met the inclusion criteria. The findings indicate that combining aerobic endurance, resistance, and respiratory training with preoperative education improves outcomes. In addition, nutritional counseling and brief relaxation/emotion-regulation strategies appear to be valuable components of multimodal prehabilitation programs, though evidence is limited. Conclusion: Preoperative rehabilitation interventions have the potential to enhance functional reserve, reduce postoperative complications, and accelerate recovery in individuals undergoing lung resection for lung cancer.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".