The feasibility of patient-reported outcomes, physical function, and mobilization in the care pathway for head and neck cancer surgical patients
Bibliographic record
Abstract
BACKGROUND: Head and neck cancer (HNC) patients are an understudied population whose treatment often includes surgery, causing a wide range of side effects. Exercise prehabilitation is a promising tool to optimize patient outcomes and may confer additional benefits as a prehabilitation tool. The primary objective of this study was to assess the feasibility of measuring patient-reported outcomes (PROs), physical function, and in-hospital mobilization across the HNC surgical timeline in advance of a future prehabilitation trial. The secondary objective was to examine potential changes in these outcomes across the surgical timeline. METHODS: HNC patients scheduled to undergo oncologic resection with free-flap reconstruction completed assessments of PROs and physical function at three timepoints across the surgical timeline (baseline, in-hospital, and postsurgical/outpatient). Mobilization was measured during the in-hospital period. The feasibility of recruitment and measurement completion was tracked, as were changes in both PROs and physical function. RESULTS: Of 48 eligible patients, 16 enrolled (recruitment rate of 33%). The baseline and in-hospital PROs were completed by 88% of participants, while the outpatient assessments were completed by 81% of participants. The baseline and in-hospital assessment of physical function were completed by 56% of participants, and 38% completed the outpatient assessment. Measuring in-hospital mobilization was completed for 63% of participants. CONCLUSION: Measuring PROs and in-hospital mobilization is feasible across the surgical timeline in HNC; however, the in-person assessment of physical function prior to surgery was not feasible. A multidisciplinary collaboration between exercise specialists and clinicians supported the development of new clinical workflows in HNC surgical care that will aid in the implementation of a future prehabilitation trial for this patient population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".