Short-course early outpatient remdesivir prevents severe disease due to COVID-19 in organ transplant recipients during the omicron BA.2 wave
Bibliographic record
Abstract
Solid organ transplant recipients (SOTr) remain at risk of severe COVID-19. Several previous early therapies are no longer effective against new circulating variants. We performed a prospective cohort study in outpatient adult SOTr during the omicron BA.2 wave (April-May 2022), to determine the effectiveness of 3 doses of remdesivir given within 7 days of symptoms onset. Patients were followed for at least 30 days. The primary outcome was hospitalization. Of 210 SOTr that had COVID-19, we included 192. The median age was 54.5 years and 61.5% were men. The most common transplants were kidney (41.7%), lung (19.3%), liver (18.8%), and heart (6.3%). Most patients (90.1%) had previously received ≥3 COVID-19 vaccine doses. Fifteen (7.8%) were hospitalized, 5(2.6%) required supplemental oxygen, 3(1.6%) ICU admission, and 2(1%) mechanical ventilation with 2(1%) deaths. Age, the number of comorbidities, prednisone chronic treatment, and lung transplant were risk factors for hospitalization. Early remdesivir significantly decreased the hospitalization rate: adjusted hazard ratio 0.12 (95% CI: 0.03-0.57). The adjusted number needed to treat to prevent one hospitalization was 15.2 (95% CI: 13.6-31.4). No patient that received early remdesivir needed ICU admission or died. In a cohort of SOTr with COVID-19 infection, administration of 3-dose early remdesivir independently reduced the disease severity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".