Radiographic Hepatic Steatosis Is Not Associated With Key Clinical Outcomes Among Patients Hospitalized With COVID-19
Bibliographic record
Abstract
BACKGROUND: Metabolic syndrome increases adverse outcomes in coronavirus disease 2019 (COVID-19) infection. Hepatic steatosis may increase risk of COVID-19 severity. Current studies evaluating steatosis lack reliable definitions. We aimed to evaluate the association of radiographic hepatic steatosis and clinical outcomes of COVID-19 severity in a diverse cohort. METHODS: We retrospectively identified patients with COVID-19 infection admitted to two US academic hospitals. Outcomes were length of stay, intensive care unit use, mechanical ventilation, and in-hospital mortality. We used Mann-Whitney U-test for continuous measures and Chi-square or Fisher's exact test for categorical measures. Multivariable linear and logistic regression analyses were used to adjust for confounders. RESULTS: Of the 319 patients, 14% had hepatic steatosis. There were no differences in length of stay (6 (4 - 16) vs. 9 (4 - 18) days, P = 0.6), intensive care unit (24% vs. 32%, P = 0.3), mechanical ventilation (28% vs. 38%, P = 0.32), or in-hospital mortality (7% vs. 17%, P = 0.12). After adjustment, there was no difference in length of stay (β: -14.37, 95% confidence interval (CI): -30.5 - 1.77, P = 0.08), intensive care unit (odds ratio (OR): 0.31, 95% CI: 0.03 - 1.09, P = 0.06), mechanical ventilation (OR: 0.13, 95% CI: 0.02 - 1.09, P = 0.06), or in-hospital mortality (OR: 0.27, 95% CI: 0.06 - 1.16, P = 0.08) among patients with hepatic steatosis. CONCLUSION: Radiographic hepatic steatosis was not associated with worse outcomes among patients hospitalized with COVID-19.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".