Early detection of COVID-19 mortality risk using non-invasive clinical characteristics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract With no effective treatment currently available and maximum preventive measures already in place, more interventions in the clinical field are needed to decrease COVID-19 patient mortality. Early prediction of mortality risk in COVID-19 patients can decrease mortality by assuring efficient resource allocation and treatment planning. This study conducts an early prediction of COVID-19 prognosis using laboratory, clinical, and demographic data collected from patients in the first day of admission. Three machine learning models were developed to investigate and compare the prediction power of invasive and noninvasive biomarkers. The results suggest that early mortality prediction of patients via non-invasive biomarkers provides significant accuracy and can be used as a triage assisting tool without the need for additional costs or waiting time of laboratory tests.
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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 | 0.072 | 0.621 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.029 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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