Hospital admission from the emergency department for selected emergent diagnoses during the first year of the COVID-19 pandemic in Ontario: a retrospective population-based study
Bibliographic record
Abstract
BACKGROUND: Avoidance of care during the pandemic may have contributed to delays in care, and as a result, worse patient outcomes. We evaluated markers of illness acuity on presentation to the emergency department among patients with non-COVID-19-related emergent diagnoses and associated outcomes. METHODS: We conducted a retrospective study using linked administrative data from Ontario. We selected 4 emergent diagnoses, namely appendicitis, ectopic pregnancy, renal failure and diabetic ketoacidosis. We used the nonemergent diagnosis of cellulitis as a control. Our primary outcome of interest was hospital admission. Secondary outcomes were ambulance arrival, surgical intervention, subsequent hospital admission within 30 days of discharge from the emergency department or hospital and 30-day mortality. We compared outcomes during the first year of the COVID-19 pandemic (Mar. 15-Dec. 31, 2020) with a control period (Mar. 15-Dec. 31, 2018, and Mar. 15-Dec. 31, 2019). RESULTS: Emergency department visits for all conditions initially decreased during the pandemic. During this period, patients across all study diagnoses were more likely to arrive to the emergency department via ambulance. Patients with an ectopic pregnancy had higher odds of surgery in the pandemic period (odds ratio [OR] 1.27, 95% confidence interval [CI] 1.04-1.55) but this was not observed among patients with appendicitis. Patients with renal failure had increased odds of hospital admission (OR 1.14, 95% CI 1.04-1.24) and 30-day mortality (OR 1.17, 95% CI 1.04-1.31) during the pandemic period. INTERPRETATION: The pandemic period was associated with increased arrival to the emergency department via ambulance across all study diagnoses. Although patients with renal failure had increased hospital admission and death, and patients with ectopic pregnancy had an increased risk of surgery, there were no differences in outcomes for other populations, suggesting the health care system was able to care for these patients effectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".