Neoadjuvant prehabilitation therapy for locally advanced non-small-cell lung cancer: Optimizing outcomes throughout the trajectory of care.
Bibliographic record
Abstract
e20545 Background: Prehabilitation is well established for improving outcomes in cancer surgery. Combining prehabilitation with neoadjuvant treatments may provide an opportunity to rapidly initiate cancer-directed therapy while improving functional status in preparation for local consolidation. In this proof-of-concept study, we analyzed non-small-cell lung cancer patients who underwent simultaneous prehabilitation and neoadjuvant therapy. Methods: We retrospectively analyzed all patients who underwent neoadjuvant treatment for non-small-cell lung cancer followed by curative intent surgery at the McGill University Health Center between 2015-2020. Patients who were screened for the prehabilitation program were identified. Screening included assessment of physical performance, nutritional status and signs for anxiety and depression. 6-minute-walk test was used as a functional outcome parameter of prehabilitation. Results: We identified a total of 93 patients who underwent neoadjuvant therapy. Of these, 12 patients were screened to undergo a prehabilitation program. For 1 patient surgical intervention was too soon to complete the program, 1 patient dropped out after the first and another patient was deemed fit to undergo surgery without intervention. Thus, 9 patients completed full neoadjuvant prehabilitation therapy. Postoperative median length of stay was 2 days (IQR 1-5) and there were no mortalities. We found major complications in 1 patient and minor complications (prolonged air leak) in 2 cases. Patients improved their 6-minute-walk test despite undergoing neoadjuvant treatment by a mean of 35 meters (SD 39). Conclusions: Neoadjuvant prehabilitation therapy is feasible and associated with encouraging results. The performance of all measures remains a logistic challenge. With multimodal strategies for lung cancer treatment becoming key to optimal outcomes, neoadjuvant prehabilitation therapy is a concept worthy of prospective multi-center evaluation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".