Tracheostomy in Flap‐Based Head and Neck Cancer Surgery: A Meta‐Analysis of Indications and Adverse Outcomes
Bibliographic record
Abstract
ABSTRACT Background Tracheostomy is frequently performed during flap‐based reconstruction for head and neck cancer, but predictive factors and complications are not well established. Methods A systematic review and meta‐analysis was conducted per PRISMA guidelines. Studies of adult patients undergoing free or pedicled flap reconstruction were included. Pooled tracheostomy rates, predictors, and complications were analyzed using random‐effects models. Heterogeneity was assessed with the I 2 statistic. Results Twenty‐six studies (27 029 patients) were included. The pooled tracheostomy rate was 54.6%, decreasing to 42.4% when routine tracheostomy studies were excluded. Advanced tumor stage, oropharyngeal site, bilateral neck dissection, prior radiotherapy, and smoking predicted tracheostomy. Flap type was not significantly associated. The overall complication rate was 16.3%, including airway issues (2.6%). No significant change in tracheostomy rates was observed over 30 years. Conclusions Tracheostomy use is influenced by tumor, surgical, and patient factors. Selective tracheostomy and validated risk tools may improve outcomes. Further prospective studies are needed.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.044 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".