Impact of Early Mobilization on Recovery after Major Head and Neck Surgery with Free Flap Reconstruction
Bibliographic record
Abstract
Surgery with free flap reconstruction is a standard treatment for head and neck cancer (HNC). Because of the complexity of HNC surgery, recovery can be challenging, and complications are common. One of the foundations of enhanced recovery after surgery (ERAS) is early postoperative mobilization. The ERAS guidelines for HNC surgery with free flap reconstruction recommend mobilization within 24 h. This is based mainly on evidence from other surgical disciplines, and the extent to which mobilization within 24 h improves recovery after HNC surgery has not been explored. This retrospective analysis included 445 patients from the Calgary Head and Neck Enhanced Recovery Program. Mobilization after 24 h was associated with more complications of any type (OR = 1.73, 95% CI [confidence interval] = 1.16-2.57) and more major complications (OR = 1.76; 95% CI = 1.00-3.16). When accounting for patient and clinical factors, mobilization after 48 h was a significant predictor of major complications (OR = 2.61; 95% CI = 1.10-6.21) and prolonged length of stay (>10 days; OR = 2.85, 95% CI = 1.41-5.76). This comprehensive analysis of the impact of early mobilization on postoperative complications and length of stay in a large HNC cohort provides novel evidence supporting adherence to the ERAS early mobilization recommendations. Early mobilization should be a priority for patients undergoing HNC surgery with free flap reconstruction.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".