A population-based analysis of the impact of the COVID-19 pandemic on common abdominal and gynecological emergency department visits
Bibliographic record
Abstract
BACKGROUND: Reduced use of the emergency department during the COVID-19 pandemic may result in increased disease acuity when patients do seek health care services. We sought to evaluate emergency department visits for common abdominal and gynecologic conditions before and at the beginning of the pandemic to determine whether changes in emergency department attendance had serious consequences for patients. METHODS: We conducted a population-based analysis using administrative data to evaluate the weekly rate of emergency department visits pre-COVID-19 (Jan. 1-Mar. 10, 2020) and during the beginning of the COVID-19 pandemic (Mar. 11-June 30, 2020), compared with a historical control period (Jan. 1-July 1, 2019). All residents of Ontario, Canada, presenting to the emergency department with appendicitis, cholecystitis, ectopic pregnancy or miscarriage were included. We evaluated weekly incidence rate ratios (IRRs) of emergency department visits, management strategies and clinical outcomes. RESULTS: Across all study periods, 39 691 emergency department visits met inclusion criteria (40.2 % appendicitis, 32.1% miscarriage, 21.3% cholecystitis, 6.4% ectopic pregnancy). Baseline characteristics of patients presenting to the emergency department did not vary across study periods. After an initial reduction in emergency department visits, presentations for cholecystitis and ectopic pregnancy quickly returned to expected levels. However, presentations for appendicitis and miscarriage showed sustained reductions (IRR 0.61-0.80), with 1087 and 984 fewer visits, respectively, after the start of the pandemic, relative to 2019. Management strategies, complications and mortality rates were similar across study periods for all conditions. INTERPRETATION: Although our study showed evidence of emergency department avoidance in Ontario during the first wave of the COVID-19 pandemic, no adverse consequences were evident. Emergency care and outcomes for patients were similar before and during the pandemic.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".