Fiber‐optic endoscopic evaluation of swallowing (FEES): Predictor of swallowing‐related complications in the head and neck cancer population
Bibliographic record
Abstract
BACKGROUND: The treatment of head and neck cancer is associated with significant dysphagia and morbidity. Prescribing a safe oral diet in this population is challenging. METHODS: Data from 116 consecutive patients having 189 fiber-optic endoscopic evaluation of swallowing (FEES) examinations over a 3-year period were analyzed. All patients had been treated for head and neck cancer and subsequently were assessed by FEES. The primary outcome was the incidence of swallowing-related adverse events resulting from the FEES-based dietary recommendations. RESULTS: There were 10 episodes of aspiration pneumonia, 4 episodes of airway obstruction, 3 unanticipated insertions of gastrostomy tubes, and 2 unexplained deaths within the study period. The overall rate of adverse events was 10.1%. The only statistically significant predictor of adverse events was the Rosenbek score (p = .03). CONCLUSIONS: Our experience is that FEES guides appropriate and safe diet recommendations in this population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".