Adding exercise to a care pathway for patients undergoing surgery for head and neck cancer
Bibliographic record
Abstract
Head and neck cancer (HNC) surgical patients experience high symptom burden. Exercise prehabilitation has the potential to improve patient outcomes, but the perspectives of patients and healthcare providers (HCPs) must be considered to facilitate implementation. The purpose of this study was to obtain qualitative feedback from HNC surgical patients and HCPs regarding: (1) adding patient assessments across the HNC surgical timeline, and (2) the logistics and potential benefits of a future exercise prehabilitation intervention. Semi-structured interviews took place with patients and HCPs. Interview questions included satisfaction with study recruitment, measurement completion, impact on clinical workflow (HCPs), and perceptions of a future prehabilitation program. Transcripts were analyzed using a constructivist philosophy and interpretive description methodology. Ten patients and ten HCPs participated in this study. Four themes were identified: (1) the value of exercise and its importance in clinical care, (2) acceptability and necessity of assessments, (3) factors to support implementation, and (4) the components of an ideal prehabilitation program. Overall, these findings highlight the importance and value of exercise across the HNC surgical timeline from both the patient and HCPs’ perspective. These findings will inform the future implementation of a multiphasic exercise prehabilitation trial in HNC surgical patients.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".