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 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".