Wearable activity trackers and mobilization after major head and neck cancer surgery: You can't improve what you don't measure
Bibliographic record
Abstract
Major surgery involving resection and free flap reconstruction is a mainstay of head and neck cancer (HNC) treatment, but postoperative morbidity and complications are common. One of the foundations for better surgical outcomes is early mobilization, which is included in enhanced recovery guidelines for all surgical specialties. However, a major unsolved challenge with early mobilization after surgery is quantifying how much a patient moves. To date, mobilization after major HNC surgery has been reported as the time to mobilization, i.e. the interval between the date of surgery and the date of the initial meaningful mobilization. Other data on postoperative mobilization in these patients are limited. Although clinicians can document mobilization via multidisciplinary progress notes, an estimate of mobilization for each postoperative day would be subjective and based on observations from several clinicians and/or the recall of the patient. Advancing research on postoperative mobilization requires the ability to objectively measure patient activity, particularly ambulatory activity, without placing a further burden on the inpatient team. Wearable activity trackers may provide a solution. Data from other surgical specialties indicate that such objective monitoring of patient ambulation in real-time to support interventions to increase mobilization may provide opportunities to improve clinical care. Objective measurement of step counts after HNC surgery would lead to an understanding of the dose-response relationship (the required quantity and frequency of mobilization that is safe and beneficial). In conclusion, integration of wearable activity trackers in the care plan for patients undergoing HNC surgery will facilitate the measurement and improvement of postoperative mobilization to reduce complications, improve surgical outcomes and enhance patient recovery.
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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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".