Oral nirmatrelvir-ritonavir for COVID-19 in higher risk outpatients
Bibliographic record
Abstract
BACKGROUND: Nirmatrelvir-ritonavir reduced progression to severe SARS-CoV2 infection in unvaccinated high-risk outpatients. The effectiveness of nirmatrelvir-ritonavir in vaccinated populations has yet to be demonstrated. METHODS: In two open-label platform trials (PANORAMIC in the UK and CanTreatCOVID in Canada), community-dwelling adults (aged ≥ 50 or ≥18 with comorbidities) who tested positive for SARS-CoV-2 and were unwell for ≤5 days were randomized to receive either usual care plus nirmatrelvir-ritonavir (300mg/100mg BID x 5 days) or to usual care alone. Primary outcome was all-cause hospitalization or death within 28 days. RESULTS: From December 8, 2021 to September 30, 2024, 3516 participants in PANORAMIC and 716 in CanTreatCOVID were randomized. Nirmatrelvir–ritonavir did not reduce hospitalization or death ((PANORAMIC: 4/1698 (0.8%) vs 11/1673 (0.7%); adjusted-odds ratio [OR], 1.18; 95% Bayesian credible interval [BCI], 0.55 to 2.62; probability of superiority 0.505, CanTreatCOVID: 2/343 (0.6%) vs 4/324 (1.2%); adjusted-OR, 0.48; 95% BCI, 0.08 to 2.23; probability of superiority 0.830). Nirmatrelvir–ritonavir increased early sustained recovery and reduced self-reported time-to-recovery In nirmatrelvir–ritonavir. Viral load was reduced by the end of treatment. 10 serious adverse events were reported for nirmatrelvir-ritonavir in PANORAMIC and 4 in CanTreatCOVID. CONCLUSIONS: In both open-label trial, nirmatrelvir–ritonavir did not reduce the incidence of hospitalization and/or death in vaccinated high-risk patients, but reduced self-reported time-to-recovery. (PANORAMIC funding: National Institute for Health and Care Research; EudraCT Number 2021-005748-31; CanTreatCOVID: Canadian Institutes of Health Research (CIHR) and Health Canada, supported by Public Health Agency of Canada; ClinicalTrials.gov number NCT05614349).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".