Outcomes for upper gastrointestinal bleeding during the first wave of the COVID‐19 pandemic in the Toronto area
Bibliographic record
Abstract
BACKGROUND AND AIM: Changes to endoscopy service availability during the COVID-19 pandemic may have affected management of upper gastrointestinal bleeding (UGIB). The aim of this study was to describe the impact of the pandemic on UGIB outcomes in the Toronto area in Canada. METHODS: We described all adults admitted to general medicine wards or intensive care units at six hospitals in Toronto and Mississauga, Canada, with UGIB during the first wave of the COVID-19 pandemic (March 1 to June 30, 2020) and compared them with a historical cohort (March 1 to June 30, 2018 and 2019). We compared clinical outcomes (in-hospital mortality, length of stay, 30-day readmission, intensive care utilization, receipt of endoscopy, persistent bleeding, receipt of second endoscopy, and need for angiographic or surgical intervention) using multivariable regression models, controlling for demographics, comorbidities, and severity of clinical presentation. RESULTS: There were 82.5 and 215.5 admissions per month for UGIB during the COVID-19 and control periods, respectively. There were no baseline differences between groups for demographic characteristics, comorbidities, or severity of bleeding. Patients in the COVID-19 group did not have significantly different unadjusted (3.9% vs 4.2%, P = 0.983) or adjusted mortality (adjusted odds ratio [OR] = 0.64, 95% confidence interval [CI] = 0.25-1.48, P = 0.322). Patients in COVID-19 group were less likely to receive endoscopy for UGIB in the unadjusted (61.8% vs 71.0%, P = 0.003) and adjusted (adjusted OR = 0.64, 95% CI = 0.49-0.84, P < 0.01) models. There were no differences between groups for other secondary outcomes. CONCLUSIONS: While patients admitted for UGIB during the first wave of the pandemic were less likely to receive endoscopy, this had no impact on mortality or any secondary outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".