Derivation and Validation of Clinical Prediction Rules for COVID-19 Mortality in Ontario, Canada
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently causing a high-mortality global pandemic. The clinical spectrum of disease caused by this virus is broad, ranging from asymptomatic infection to organ failure and death. Risk stratification of individuals with coronavirus disease 2019 (COVID-19) is desirable for management, and prioritization for trial enrollment. We developed a prediction rule for COVID-19 mortality in a population-based cohort in Ontario, Canada. Methods Data from Ontario’s provincial iPHIS system were extracted for the period from January 23 to May 15, 2020. Logistic regression–based prediction rules and a rule derived using a Cox proportional hazards model were developed and validated using split-halves validation. Sensitivity analyses were performed, with varying approaches to missing data. Results Of 21 922 COVID-19 cases, 1734 with complete data were included in the derivation set; 1796 were included in the validation set. Age and comorbidities (notably diabetes, renal disease, and immune compromise) were strong predictors of mortality. Four point-based prediction rules were derived (base case, smoking excluded, long-term care excluded, and Cox model–based). All displayed excellent discrimination (area under the curve for all rules > 0.92) and calibration (P > .50 by Hosmer-Lemeshow test) in the derivation set. All performed well in the validation set and were robust to varying approaches to replacement of missing variables. Conclusions We used a public health case management data system to build and validate 4 accurate, well-calibrated, robust clinical prediction rules for COVID-19 mortality in Ontario, Canada. While these rules need external validation, they may be useful tools for management, risk stratification, and clinical trials.
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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 | 0.001 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it