The COVID‐19 Worsening Score (COWS)—a predictive bedside tool for critical illness
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: To evaluate the accuracy of a new COVID-19 prognostic score based on lung ultrasound (LUS) and previously validated variables in predicting critical illness. METHODS: We conducted a single-center retrospective cohort development and internal validation study of the COVID-19 Worsening Score (COWS), based on a combination of the previously validated COVID-GRAM score (GRAM) variables and LUS. Adult COVID-19 patients admitted to the emergency department (ED) were enrolled. Ten variables previously identified by GRAM, days from symptom onset, LUS findings, and peripheral oxygen saturation/fraction of inspired oxygen (P/F) ratio were analyzed. LUS score as a single predictor was assessed. We evaluated GRAM model's performance, the impact of adding LUS, and then developed a new model based on the most predictive variables. RESULTS: Among 274 COVID-19 patients enrolled, 174 developed critical illness. The GRAM score identified 51 patients at high risk of developing critical illness and 132 at low risk. LUS score over 15 (range 0 to 36) was associated with a higher risk ratio of critical illness (RR, 2.05; 95% confidence interval [CI], 1.52-2.77; area under the curve [AUC], 0.63; 95% CI 0.676-0.634). The newly developed COVID-19 Worsening Score relies on five variables to classify high- and low-risk patients with an overall accuracy of 80% and negative predictive value of 93% (95% CI, 87%-98%). Patients scoring more than 0.183 on COWS showed a RR of developing critical illness of 8.07 (95% CI, 4.97-11.1). CONCLUSIONS: COWS accurately identify patients who are unlikely to need intensive care unit (ICU) admission, preserving resources for the remaining high-risk patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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