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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".