Predefined and data driven CT densitometric features predict critical illness and hospital length of stay in COVID-19 patients
Bibliographic record
Abstract
Abstract The aim of this study was to compare predefined and data-driven parameters of whole lung CT density histograms to predict critical illness outcome and hospital length of stay in a cohort of 80 COVID-19 patients. CT chest images on segmented lungs were retrospectively analyzed. Functional Principal Component Analysis (FPCA) was used to find the main modes of variations on CT density histograms (F1,F2,F3,F4) in the whole patient cohort. The data driven and a priori CT density features, the CT severity score, the COVID-GRAM score and the patient clinical data were assessed for predicting the patient outcome using logistic regression models stratified for contrast enhanced CT and non-enhanced CT, and survival analysis. ROC analysis identified as best predictors of critically ill status: 87.5th percentile CT density (Q875) - AUC: 0.88 95%CI (0.79 0.94), F1-CT - AUC: 0.87 (0.77 0.93) Standard Deviation (SD-CT)- AUC: 0.86 (0.73, 0.93). Multivariate models combining CT-density predictors and Neutrophil-Lymphocyte Ratio showed the highest accuracy with cross-validated AUCs in the 0.91–0.92 range for contrast CT and 0.82–0.88 range for non-contrast CT. SD-CT, Q875 and F1 score were significant predictors of hospital length of stay while controlling for hospital death using competing risks models. Predefined and data-driven parameters of lung CT density histograms can predict critical illness and length of stay to guide management and resources. FPCA method can be used to interpret the CT density histogram variation in a patient cohort and to extract predictive features with minimal a priori knowledge.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".