Navigating Care Amid Crisis: The Impact of the COVID-19 Pandemic on Eosinophilic Esophagitis Management in Canada
Bibliographic record
Abstract
Background and Objectives: The COVID-19 pandemic caused significant disruptions in healthcare services. Foreign body impactions (FBIs), with Eosinophilic Esophagitis (EoE) being one of the leading underlying causes in adults, are some of the most common emergencies and often require endoscopy. The study assesses the impact of COVID-19 on the incidence and outcomes of foreign body impactions (FBIs) requiring endoscopy at Canadian tertiary centres in a single city. Methods: Patients presenting to tertiary care hospital emergency departments in Calgary (March 2019–Feb 2022) for FBI were identified using the AACRS (Alberta Ambulatory Care Reporting System) database using International Classification of Disease (ICD-9 and ICD-10) codes (T178, T181) and provincial diagnostic codes (935.1, 530.4) for a foreign body in the esophagus (530.13 and K20.0). One-way ANOVA (SPSS® 27.0) analyzed incidence and disease progression across Pre-COVID-19 and COVID-19 years. Results: 759 patients were included in the analysis (274 Pre-COVID-19 (PC: March 2019–Feb 2020), 234 COVID-19 Year 1 (CY1: March 2020–Feb 2021), and 251 COVID-19 Year 2 (CY2: March 2021–Feb 2022)). The mean age remained consistent, with two-thirds being male. Food was the predominant type of FBI (>90%). The incidence of new EoE in EDs declined from PC (60.9%) to CY1 (47.4%) (p < 0.001), while endoscopic resolution remained >96%. Follow-up endoscopies in outpatient settings remained stable (~60%). Non-EoE causes of FBI, including esophagitis and cancer, increased in CY2. The mean ED length of stay rose in CY2, but this was not statistically significant (p = 0.06). Conclusions: This study highlights the resilience of emergent endoscopic care in Calgary during COVID, despite a decline in new EoE diagnoses, which might be due to access barriers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".