Relationship Between Viremia and Specific Organ Damage in Ebola Patients: A Cohort Study
Bibliographic record
Abstract
Background: Pathogenesis of Ebola virus disease remains poorly understood. We used concomitant determination of routine laboratory biomarkers and Ebola viremia to explore the potential role of viral replication in specific organ damage. Methods: We recruited patients with detectable Ebola viremia admitted to the EMERGENCY Organizzazione Non Governativa Organizzazione Non Lucrativa di Utilità Sociale (ONG ONLUS) Ebola Treatment Center in Sierra Leone. Repeated measure of Ebola viremia, alanine aminotransferase (ALT), aspartate aminotransferase (AST), bilirubin, creatine phosphokinase (CPK), lactate dehydrogenase (LDH), activated prothrombin time (aPTT), international normalized ratio (INR), creatinine, and blood urea nitrogen (BUN) were recorded. Patients were followed up from admission until death or discharge. Results: One hundred patients (49 survivors and 51 nonsurvivors) were included in the analysis. Unadjusted analysis to compare survivors and nonsurvivors provided evidence that all biomarkers were significantly above the normal range and that the extent of these abnormalities was generally higher in nonsurvivors than in survivors. Multivariable mixed-effects models provided strong evidence for a biological gradient (suggestive of a direct role in organ damage) between the viremia levels and either ALT, AST, CPK LDH, aPTT, and INR. In contrast, no direct linear association was found between viremia and either creatinine, BUN, or bilirubin. Conclusions: This study provides evidence to support that Ebola virus may have a direct role in muscular damage and imbalance of the coagulation system. We did not find strong evidence suggestive of a direct role of Ebola virus in kidney damage. The role of the virus in liver damage remains unclear, but our evidence suggests that acute severe liver injury is not a typical feature of Ebola virus disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".