Effect of the COVID-19 pandemic on emergency department utilization of computed tomography scans of appendicitis and diverticulitis
Bibliographic record
Abstract
PURPOSE: Investigating the effect of the COVID-19 lockdown on adult patient visits, computed tomography (CT) abdominal scans, and presentations of appendicitis and diverticulitis, to emergency departments (ED) in St. John's NL. METHODS: A retrospective quantitative analysis was applied, using ED visits and Canadian Triage and Acuity Scale (CTAS) scores. mPower (Nuance Communications, UK) identified CT abdominal scan reports, which were categorized into (1) normal/other, (2) appendicitis, or (3) diverticulitis. Time intervals included pre-lockdown (January-February), lockdown (March-June), and post-lockdown (July-August). Data from 2018 to 2019 (January-August) were used to generate expected patient volumes for 2020, and pre- and post-lockdown were included to control for other variables outside the lockdown. RESULTS: Chi-squared goodness of fit tested for deviations from predicted means for 2018-2019. Compared to expectations, daily ED visits from January to August 2020 showed a significant (p < 0.001) decrease in patient volumes independent of gender, age, and CTAS scores. During and post-lockdown, CT abdominal scans did not drop in proportion to patient volume. Appendicitis presentations remained indifferent to lockdown, while diverticulitis presentations appeared to wane, with no difference in combined complicated cases in comparison to what was expected. CONCLUSION: During lockdown, significantly fewer patients presented to the ED. The proportion of ordered CT abdominal scans increased significantly per person seen, without change in CTAS scores. Considering combined pathology cases increased during the lockdown, ED physicians were warranted in increasing abdominal imaging as patients did not avoid the ED. This may have resulted from a change in clinical practice where the uncertainty of COVID-19 increased CT scan usage.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".