Real-world effectiveness of nirmatrelvir/ritonavir use for COVID-19: A population-based cohort study in Ontario, Canada
Bibliographic record
Abstract
Abstract Background Our objective was to evaluate the real world effectiveness of nirmatrelvir/ritonavir to prevent severe COVID-19 while Omicron and its subvariants predominate. Methods We conducted a population based cohort study in Ontario, Canada including all residents >17 years of age who tested positive for SARS-CoV-2 by PCR between 4 April and 31 August 2022. We compared nirmatrelvir/ritonavir treated patients to unexposed patients and measured the primary outcome of hospitalization or death from COVID-19, and a secondary outcome of death 1-30 days. We used weighted logistic regression to calculate weighted odds ratios (wOR) with 95% confidence intervals (CIs) using inverse probability of treatment weighting (IPTW) to control for confounding. Results The final cohort included 177,545 patients with 8,876 (5.0%) exposed and 168,669 (95.0%) unexposed individuals. The groups were well balanced with respect to demographic and clinical characteristics after applying stabilized IPTW. Hospitalization or death within 30 days was lower in the nirmatrelvir/ritonavir treated group compared to unexposed individuals (2.1% vs 3.7%, wOR 0.56; 95%CI, 0.47-0.67). In the secondary analysis, the relative odds of death was also significantly reduced (1.6% vs 3.3%, wOR 0.49; 95%CI, 0.39-0.62). The number needed to treat to prevent one case of severe COVID-19 was 62 (95%CI 43 to 80). Findings were similar across strata of age, DDIs, vaccination status, and comorbidities. Interpretation Nirmatrelvir/ritonavir was associated with significantly reduced risk of hospitalization and death from COVID-19 in this observational study, supporting ongoing use of this therapeutic to treat patients with mild COVID-19 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".