P.081 Epidemiology of Neurological and Cardiac Complications of COVID-19 among Ontario Visible Minorities: A Retrospective Study of Chinese and South Asian Canadians
Bibliographic record
Abstract
Background: This is a population-based retrospective study of neurological and cardiac complications of COVID-19 among Ontario visible minorities: Chinese and South Asian Canadians Methods: From January 1, 2020 to September 30, 2020, using the last name algorithm, rates and types of cardiac and neurological complication of these two cohorts along with the general population in Ontario with COVID-19 were analysed by Institue of Clinical Evaluative Sciences. Results: Preliminary results show that Chinese-Canadians (N= 1,186) with COVID-19 are older with a mean age of 50.74 years old compared to general population (N= 42,547) of 47.57 years old (P< .001), while South Asians (N= 3,459) have a younger mean age of 42.08 years old (P< .001). Total cardiac and neurological complication rates, hospitalization rates and ICU admission rates are all higher for Chinese-Canadians while they are lower in South Asians and all achieving statistical significance (P < .001). Overall mortality rate is significantly higher for Chinese-Canadians at 8.1% vs 5.0% general population (P < .001). Conclusions: Chinese-Canadians with COVID-19 in Ontario were much older and have higher cardiac and neurological complication rates and overall mortality rate than the general population. These data have significant implications for proper prevention and appropriate management for these vulnerble elderly Chinese-Canadians.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".