Clinical Outcomes of COVID-19 Patients Treated with Convalescent Plasma or Remdesivir Alone and in Combination at a Community Hospital in California’s Central Valley
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to compare how treatment with convalescent plasma (CP) monotherapy, remdesivir (RDV) monotherapy, and combination therapy (CP + RDV) in patients with COVID-19 affected clinical outcomes. METHODS: Patients with COVID-19 infection who were admitted to the hospital received CP, RDV, or combination of both. Mortality, discharge disposition, hospital length of stay (LOS), intensive care unit (ICU) LOS, and total ventilation days were compared between each treatment group and stratified by ABO blood group. An exploratory analysis identified risk factors for mortality. Adverse effects were also evaluated. RESULTS: RDV monotherapy showed an increased chance of survival compared to combination therapy or CP monotherapy (p = 0.052). There were 15, 3, and 6 deaths in the CP, RDV, and combination therapy groups, respectively. The combination therapy group had the longest median ICU LOS (8, IQR 4.5-15.5, p = 0.220) and hospital LOS (11, IQR 7-15.5, p = 0.175). Age (p = 0.036), initial SOFA score (p = 0.013), and intubation (p = 0.005) were statistically significant predictors of mortality. Patients with type O blood had decreased ventilation days, ICU LOS, and total LOS. Thirteen treatment-related adverse events occurred. CONCLUSION: No significant differences in clinical outcomes were observed between patients treated with RDV, CP, or combination therapy. Elderly patients, those with a high initial SOFA score, and those who require intubation are at increased risk of mortality associated with COVID-19. Blood type did not affect clinical outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".