RETRACTED AND REPLACED: Real-World Effectiveness of Nirmatrelvir/Ritonavir on Coronavirus Disease 2019–Associated Hospitalization Prevention: A Population-based Cohort Study in the Province of Quebec, Canada
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
BACKGROUND: Nirmatrelvir/ritonavir has shown to reduce COVID-19 hospitalization and death before Omicron, but updated real-world evidence studies are needed. This study aimed to assess whether nirmatrelvir/ritonavir reduces the risk of COVID-19-associated hospitalization among high-risk outpatients. METHODS: A retrospective cohort study of outpatients with SARS-CoV-2 between March 15 and 15 October 2022, using data from the Quebec clinico-administrative databases. Outpatients treated with nirmatrelvir/ritonavir were compared with infected ones not receiving nirmatrelvir/ritonavir using propensity-score matching. Relative risk (RR) of COVID-19-associated hospitalization within 30 days was assessed using a Poisson regression. RESULTS: A total of 8402 treated outpatients were matched to controls. Regardless of vaccination status, nirmatrelvir/ritonavir treatment was associated with a 69% reduced RR of hospitalization (RR: .31; 95% CI: .28; .36; number needed to treat [NNT] = 13). The effect was more pronounced in outpatients with incomplete primary vaccination (RR: .04; 95% CI: .03; .06; NNT = 8), while no benefit was found in those with a complete primary vaccination (RR: .93; 95% CI: .78; 1.08). Subgroups analysis among high-risk outpatients with a complete primary vaccination showed that nirmatrelvir/ritonavir treatment was associated with a significant decrease in the RR of hospitalization in severely immunocompromised outpatients (RR: .66; 95% CI: .50; .89; NNT = 16) and in high-risk outpatients aged ≥70 years (RR: .50; 95% CI: .34; .74; NNT = 10) when the last dose of the vaccine was received at least 6 months ago. CONCLUSIONS: Nirmatrelvir/ritonavir reduces the risk of COVID-19-associated hospitalization among incompletely vaccinated high-risk outpatients and among some subgroups of completely vaccinated high-risk outpatients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".