A142 APPROPRIATENESS OF POST-ENDOSCOPY CARE IN PATIENTS PRESENTING WITH FOOD BOLUS IMPACTIONS OVERNIGHT: A POPULATION-BASED MULTICENTER COHORT STUDY
Bibliographic record
Abstract
Abstract Background Appropriate management of esophageal food bolus impactions includes endoscopic evaluation and follow-up for potential underlying esophageal pathology. Patients who present with impactions at night may not receive optimal long-term post-endoscopy care due to patient-, physician-, or system-related factors. Purpose We aimed to evaluate the appropriateness of care for patients who present with food bolus impactions after regular daytime hours. Method We conducted a retrospective, population-based, multi-center cohort study of adult patients undergoing endoscopy for food impaction between 19:00-06:59 from 2016-2018 in the Calgary Health Zone, Canada. Appropriate post-endoscopy care was defined by a composite of a follow-up clinic visit, repeat endoscopy, other appropriate investigations (e.g., manometry), or appropriate medical treatment (e.g., proton pump inhibitor). Predictors of inappropriate care were assessed using multivariable logistic regression, expressed as adjusted odds ratios (aOR) with 95% confidence intervals (CI). Result(s) A total of 323 patients underwent an after-hours or overnight endoscopy for food bolus impaction. 25.4% (82/323) of patients did not receive appropriate post-endoscopy care. Predictors of inappropriate care included rural residence (aOR 2.66 [95% CI: 1.18-6.01], p=0.02), first food bolus presentation (aOR 2.38 [95% CI: 1.04-5.44], p=0.04), and absence of a specific pathology during the index procedure (aOR 3.01 [95% CI: 0.97-9.29], p=0.05), suggesting a potential association with clinician cognitive bias. Among patients who were followed, 18.9% (35/185) had a change in the original diagnosis. Image Conclusion(s) One quarter of patients presenting with a food bolus impaction at night do not receive appropriate post-endoscopy care. System-based interventions should target this high-risk population as the diagnosis and management may change with follow-up. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared
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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 | 0.000 |
| 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) | 0.002 | 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".