Predicting reactive gastrostomy-tube placement after head and neck free flap reconstruction
Bibliographic record
Abstract
OBJECTIVES: The objectives of the study are: 1) to analyze the factors associated with post-operative placement of gastrostomy tube (G-tube) in head and neck reconstruction patients, and 2) to create a simple scoring system that can predict G-tube dependence. MATERIALS AND METHODS: Patients who underwent head and neck free tissue transfer at the author's institution from January 2015-May 2021 were identified. Data on patient characteristics, clinical outcomes, and G-tube placement were collected. Two regression models were developed, a comprehensive model including all significant clinical variables and a practical model including the most predictive variables. RESULTS: We identified 525 head and neck free tissue transfer patients, of whom 63 (12%) required G-tube placement. The comprehensive model revealed a ROC curve AUC of 0.897 and identified the following significant variables: age (OR 1.04, 1.01-1.08 95% CI), CCI (OR 1.35, 1.14-1.62 95% CI), primary lesion site: oropharynx (OR 4.58, 1.68-12.3 95% CI), flap harvested: ALT (OR 3.69, 1.46-9.33, 95% CI), tracheostomy (OR 4.04, 1.41-13.4 95% OR), and bilateral neck dissection (OR 2.33, 0.99-5.39 95% CI). Based on these variables, a simple 9-point scoring system was created to assess the risk of post-operative G-tube dependence and could accurately make predictions in up to 92.3% of patients. CONCLUSION: Risk factors for G-tube dependence were identified using the largest single-center database of head and neck reconstruction patients. This is the first report of the association of ALT free flaps and G-tube dependence. The proposed scoring system could be implemented prospectively to help determine prophylactic G-tube placement.
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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".