Describing the role of a COVID assessment centre during the early phase of the COVID-19 pandemic in Ontario, Canada
Bibliographic record
Abstract
Background: Little has been written about the role of COVID assessment centres set up during the COVID-19 pandemic. Methods: This descriptive study reviewed and compared COVID cases presenting to the Mount Sinai Hospital COVID assessment centre and emergency departments over a 4-month period at the start of the pandemic in 2020. Results: Of 185 COVID-positive presentations, 115 (62.2%) were assessed in the COVID assessment centre and 70 (37.8%) in the emergency department. Patients seen in the COVID assessment centre tended to be younger (mean age 33.5 years) than in the emergency department (mean age 51.8 years, P < .001), had fewer comorbidities ( P ≤ .05 for hypertension, congestive heart failure, diabetes, and cancer), and were less likely to have shortness of breath, fever, or focal lung findings ( P < .01 for all). Chest imaging was ordered for 57.1% of emergency department cases versus 0% for COVID assessment centre cases ( P < .001). Overall, 21 out of 69 (30.4%) COVID-positive cases were admitted from the emergency department, while all COVID assessment centre cases were discharged home. Conclusions: The Mount Sinai COVID assessment centre assessed the majority of COVID cases early on in the pandemic at this site. While these were milder COVID infections, this decreased the overall number of COVID infections that might otherwise have needed to be seen in the emergency department.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".