Effect of COVID-19 on computed tomography usage and critical test results in the emergency department: an observational study
Bibliographic record
Abstract
Background: The effect of the coronavirus disease 2019 (COVID-19) pandemic on new or unexpected radiologic findings in the emergency department (ED) is unclear. The aim of this study was to determine the effect of the COVID-19 pandemic on the number of computed tomography (CT) critical test results in the ED. Methods: We performed a retrospective observational study of ED CT usage at 4 Ontario hospitals (1 urban academic, 1 northern academic, 1 urban community and 1 rural community) over 1 month during the COVID-19 pandemic (April 2020) and over the same month 1 year earlier (April 2019; before the pandemic). The CT findings from 1 of the 4 hospitals, Hamilton Health Sciences, were reviewed to determine the number of critical test results by body region. Total CT numbers were compared using Poisson regression and CT yields were compared using the χ2 test. Results: The median number of ED CT examinations per day was markedly lower during the COVID-19 pandemic than before the pandemic (82 v. 133, p < 0.01), with variation across hospitals (p = 0.001). On review of 1717 CT reports from Hamilton Health Sciences, fewer critical test results were demonstrated on CT pulmonary angiograms (43 v. 88, p < 0.001) and CT examinations of the head (82 v. 112, p < 0.03) during the pandemic than before the pandemic; however, the yield of these examinations did not change. Although the absolute number of all CT examinations with critical test results decreased, the number of CT examinations without critical results decreased more, resulting in a higher yield of CT for critical test results during the pandemic (46% [322/696] v. 37% [379/1021], p < 0.01). Interpretation: Emergency department CT volumes markedly decreased during the COVID-19 pandemic, predominantly because there were fewer examinations with new or unexpected findings. This suggests that COVID-19 public information campaigns influenced the behaviours of patients presenting to the ED.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".