Effect of Opaganib on Supplemental Oxygen and Mortality in Patients with Severe SARS-CoV-2 Pneumonia
Bibliographic record
Abstract
ABSTRACT Rationale There are few treatment options for severe COVID-19 pneumonia. Opaganib is an oral treatment under investigation. Objective Evaluate opaganib treatment in hospitalized patients with severe COVID-19 pneumonia. Methods A randomized, placebo-controlled, double-blind phase 2/3 trial was conducted in 60 sites worldwide from August 2020 to July 2021. Patients received either opaganib (n=230; 500mg twice daily) or matching placebo (n=233) for 14 days. Main Outcome Measurements Primary outcome was the proportion of patients no longer requiring supplemental oxygen by day 14. Secondary outcomes included changes in the World Health Organization Ordinal Scale for Clinical Improvement, viral clearance, intubation, and mortality at 28- and 42-days. Main Results Pre-specified primary and secondary outcome analyses did not demonstrate statistically significant benefit (except for time to viral clearance). Post-hoc analysis revealed the fraction of inspired oxygen (FiO 2 ) at baseline was prognostic for opaganib treatment responsiveness and corresponded to disease severity markers. Patients with FiO 2 levels at or below the median value (≤60%) had better outcomes after opaganib treatment (n=117) compared to placebo (n=134). The proportion of patients with ≤60% FIO2 at baseline that no longer required supplemental oxygen (≥24 hours) by day 14 of opaganib treatment increased (76.9% vs 63.4%: p-value =0.033). There was a 62.6% reduction in intubation/mechanical ventilation (6.84% vs 17.91%; p-value=0.012) and a clinically meaningful 62% reduction in mortality (5.98% vs 16.7%; p-value=0.019) by day 42. No new safety concerns observed. Conclusions Post-hoc analysis supports opaganib benefit in COVID-19 severe pneumonia patients that require lower supplemental oxygen (≤60% FiO2). Further studies are warranted. Trial registration number NCT04467840
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".