Race-based data collection among COVID-19 inpatients: A retrospective chart review
Bibliographic record
Abstract
Public health data have demonstrated disproportionate COVID-19 morbidity and mortality among racialized populations. However, limited hospital data may prevent research into racial disproportionality among inpatients. We conducted a retrospective cross-sectional study of patients admitted with or without COVID-19 to an Ontario tertiary hospital between March and October 2020 to determine the percentage of inpatients with a formal race or ethnicity assessment in their medical record. The COVID-19 group included inpatients with concurrent COVID-19 positivity; the reference group included a random sample of General Medicine inpatients without COVID-19. We reviewed 80 patients with COVID-19 and 80 patients without COVID-19. Formal ethnicity assessments were recorded among 44% of the COVID-19 group and 49% of the reference group. Race and ethnicity data collection was less than 50% among inpatients with and without COVID-19 in one Ontario hospital. Adequate data collection is necessary to study racial health disparities in the hospital setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".