Predictors of Failed and Delayed Decannulation after Head and Neck Surgery
Bibliographic record
Abstract
OBJECTIVE: To determine the variables that are predictive of failed decannulation (FD), delayed decannulation (DD), and days to decannulation in patients who underwent head and neck cancer resection with free tissue transfer reconstruction for head and neck squamous cell carcinoma. DESIGN: Case series with chart review. SETTING: Tertiary care otolaryngology-head and neck surgery referral center. SUBJECT AND METHODS: Patients (N = 108) were included who underwent head and neck cancer resection with free tissue transfer reconstruction and tracheostomy between 2011 and June 2014. Patients with laryngectomy, previous tracheostomy, and other airway pathology necessitating tracheotomy were excluded. Preoperative patient variables and cancer site/staging variables were analyzed, as well as extent of structures resected and type of reconstruction. Univariate and multivariate binary logistic and Cox regression analyses were used to determine predictors of FD and DD. Cox regression analysis was used to determine predictors of days to decannulation. RESULTS: Of the 108 included patients, 16 had FD, and 26 had DD. Univariate analysis demonstrated that advanced stage (r = 0.233, P = .021), total glossectomy (r = 0.924, P < .001), anterolateral thigh flap reconstruction (r = 0.906, P < .001), smoking at time of surgery (r = 0.319, P = .002), and pack years (r = 0.322, P = .001) were associated with FD. Cox regression analysis showed that total glossectomy, exp(B) = 15.837 (95% confidence interval [95% CI]: 1.949-128.679); anterolateral thigh flap reconstruction, exp(B) = 8.439 (95% CI: 2.435-29.620); and smoking status, exp(B) = 2.970 (95% CI: 1.617-5.456) were independent predictors of days to decannulation and FD. CONCLUSIONS: Patients with total glossectomy defects and those who continue to smoke are at increased risk for FD and DD. Aggressive smoking cessation programs may decrease the risk of FD and DD. Patients should be counseled about their risk profiles.
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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".