Effectiveness of Nirmatrelvir–Ritonavir Against the Development of Post–COVID-19 Conditions Among U.S. Veterans
Bibliographic record
Abstract
BACKGROUND: COVID-19 has been linked to the development of many post-COVID-19 conditions (PCCs) after acute infection. Limited information is available on the effectiveness of oral antivirals used to treat acute COVID-19 in preventing the development of PCCs. OBJECTIVE: To measure the effectiveness of outpatient treatment of COVID-19 with nirmatrelvir-ritonavir in preventing PCCs. DESIGN: Retrospective target trial emulation study comparing matched cohorts receiving nirmatrelvir-ritonavir versus no treatment. SETTING: Veterans Health Administration (VHA). PARTICIPANTS: Nonhospitalized veterans in VHA care who were at risk for severe COVID-19 and tested positive for SARS-CoV-2 during January through July 2022. INTERVENTION: Nirmatrelvir-ritonavir treatment for acute COVID-19. MEASUREMENTS: Cumulative incidence of 31 potential PCCs at 31 to 180 days after treatment or a matched index date, including cardiac, pulmonary, renal, thromboembolic, gastrointestinal, neurologic, mental health, musculoskeletal, endocrine, and general conditions and symptoms. RESULTS: = 9593) and their matched untreated comparators in the incidence of most PCCs examined individually or grouped by organ system, except for lower combined risk for venous thromboembolism and pulmonary embolism (subhazard ratio, 0.65 [95% CI, 0.44 to 0.97]; cumulative incidence difference, -0.29 percentage points [CI, -0.52 to -0.05 percentage points]). LIMITATIONS: Ascertainment of PCCs using International Classification of Diseases, 10th Revision, codes may be inaccurate. Evaluation of many outcomes could have resulted in spurious associations with combined thromboembolic events by chance. CONCLUSION: Out of 31 potential PCCs, only combined thromboembolic events seemed to be reduced by nirmatrelvir-ritonavir. PRIMARY FUNDING SOURCE: U.S. Department of Veterans Affairs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".