Population-based evaluation of the effectiveness of nirmatrelvir–ritonavir for reducing hospital admissions and mortality from COVID-19
Bibliographic record
Abstract
BACKGROUND: A randomized controlled trial involving a high-risk, unvaccinated population that was conducted before the Omicron variant emerged found that nirmatrelvir-ritonavir was effective in preventing progression to severe COVID-19. Our objective was to evaluate the effectiveness of nirmatrelvir-ritonavir in preventing severe COVID-19 while Omicron and its subvariants predominate. METHODS: We conducted a population-based cohort study in Ontario that included all residents who were older than 17 years of age and had a positive polymerase chain reaction test for SARS-CoV-2 between Apr. 4 and Aug. 31, 2022. We compared patients treated with nirmatrelvir-ritonavir with patients who were not treated and measured the primary outcome of hospital admission from COVID-19 or all-cause death at 1-30 days, and a secondary outcome of all-cause death. We used weighted logistic regression to calculate weighted odds ratios (ORs) with confidence intervals (CIs) using inverse probability of treatment weighting (IPTW) to control for confounding. RESULTS: The final cohort included 177 545 patients, 8876 (5.0%) who were treated with nirmatrelvir-ritonavir and 168 669 (95.0%) who were not treated. The groups were well balanced with respect to demographic and clinical characteristics after applying stabilized IPTW. We found that the occurrence of hospital admission or death was lower in the group given nirmatrelvir-ritonavir than in those who were not (2.1% v. 3.7%; weighted OR 0.56, 95% CI 0.47-0.67). For death alone, the weighted OR was 0.49 (95% CI 0.39-0.62). Our findings were similar across strata of age, drug-drug interactions, vaccination status and comorbidities. The number needed to treat to prevent 1 case of severe COVID-19 was 62 (95% CI 43-80), which varied across strata. INTERPRETATION: Nirmatrelvir-ritonavir was associated with significantly reduced odds of hospital admission and death from COVID-19, which supports use to treat patients with mild COVID-19 who are at risk for severe 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".