P.033 COVID-19: neurologic and cardiac complications among Chinese and South Asians in Ontario: waves 1-3
Bibliographic record
Abstract
Background: This is a population-based retrospective study of neurologic and cardiac complications of COVID-19 among Chinese and South Asians in Ontario during waves 1-3. Methods: Chinese and South Asians with COVID-19 were identified using a validated surname algorithm and their outcomes of mortality, and cardiac and neurologic complications with those of the general population using multivariable logistic regression models. Results: Compared to the general population (n= 439,977), the Chinese population (n= 15,208) was older (mean age 44.2 vs 40.6 years, P < 0.001) and the South Asian population (n= 46,333) was younger (39.2 years, P < 0.001). The Chinese population had a higher 30-day mortality (odds ratio [OR] 1.44; 1.28-1.61) and more hospitalization or emergency department visits(OR 1.14; 1.09-1.28), with a trend toward a higher incidence of cardiac complications (OR 1.03; 0.87-1.12) and neurologiccomplications (OR 1.23; 0.96-1.58). South Asians had a lower 30-day mortality (OR 0.88; 0.78-0.98) but a higher incidence of hospitalization or emergency department visits (OR 1.17; 1.14-1.20) with a trend toward a lower incidence of cardiac complications(OR 0.76; 0.67-0.87) and neurologic complications (OR 0.89; 0.73-1.09). Conclusions: Ethnicity continues to be an important determinant of mortality, cardiac and neurologic outcomes, and healthcare use among Ontario patients with COVID-19.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".