Prevalence of Esophageal Webs in Patients Undergoing Direct Laryngoscopy
Bibliographic record
Abstract
INTRODUCTION: The prevalence of esophageal webs in the pharyngoesophageal segment (PES) is unknown, in part because webs produce inconsistent symptomatology and can be difficult to diagnose. This study aims to determine the prevalence of PES webs in patients undergoing endoscopic laryngeal surgery. METHODS: This retrospective cohort study included patients undergoing laryngoscopy for pathology unrelated to webs. Evaluation for the presence of a web was performed on standard/routine examination of the PES during direct laryngoscopy. Demographic and clinical factors were analyzed for associations with webs. RESULTS: Of 123 patients included in this study, 42 (34.1%) were found to have a PES web. A total of 22 webs (52.4%) were on the left, 16 (38.1%) were on the right, and 4 (9.5%) were bilateral. There was no difference in age (58.1 vs. 58.7, p = 0.864) or BMI (29.7 vs. 29.8, p = 0.900) between patients with and without PES webs. Webs were significantly more common in patients with a history of irradiation to the head and neck (70% vs. 31%, p = 0.031) with a RR of 2.26 (CI: 1.38-3.69). There was no association of webs with gender, race, history of gastroesophageal reflux disease, or other clinical factors. Only 33.3% of patients with a web had documented symptoms of dysphagia. CONCLUSIONS: PES webs may be more prevalent than what is historically cited in the literature, and webs may be more common in patients with a history of irradiation to the head and neck.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".