Postendoscopy Care for Patients Presenting With Esophageal Food Bolus Impaction: A Population-Based Multicenter Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Esophageal food bolus impactions (FBI) are a common gastrointestinal emergency. Appropriate management includes not only index endoscopy for disimpaction but also medical follow-up and treatment for the underlying esophageal pathology. We evaluated the appropriateness of postendoscopy care for patients with FBI and assessed patient-related, physician-related, and system-related factors that may contribute to loss to follow-up. METHODS: We conducted a retrospective, population-based, multicenter cohort study of all adult patients undergoing endoscopy for FBI in the Calgary Health Zone, Canada, from 2016 to 2018. Appropriate postendoscopy care was defined by a composite of a clinical or endoscopic follow-up visit, appropriate investigations (e.g., manometry), or therapy (e.g., proton-pump inhibitors or endoscopic dilation). Predictors of inappropriate care were assessed using multivariable logistic regression. RESULTS: A total of 519 patients underwent endoscopy for FBI: 25.2% (131/519) did not receive appropriate postendoscopy care. Half of the patients (55.3%, 287/519) underwent follow-up endoscopy or attended clinic, and among this group, 22.3% (64/287) had a change in their initial diagnosis after follow-up, including 3 new cases of esophageal cancer. Patients in whom a suspected underlying esophageal pathology was not identified at the index endoscopy were 7-fold (adjusted odds ratio 7.28, 95% confidence interval 4.49-11.78, P < 0.001) more likely to receive inappropriate postendoscopy follow-up and treatment, even after adjusting for age, sex, rural residence, timing of endoscopy, weekend presentation, and endoscopic interventions. DISCUSSION: One-quarter of patients presenting with an FBI do not receive appropriate postendoscopy care. This is strongly associated with failure to identify a potential underlying pathology at index presentation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".