Real-world evidence of sotrovimab effectiveness for preventing severe outcomes in patients with COVID-19: A quality improvement propensity-matched retrospective cohort study of a pan-provincial program in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVES: Post-marketing surveillance of sotrovimab's effect during implementation in the Canadian population is limited. METHODS: The study used a propensity score-matched retrospective cohort design. Follow-up began between the periods of December 15, 2021 and April 30 2022. The study assessed any severe outcome defined as all-cause hospital admission or mortality within 30 days of a confirmed COVID-19-positive test. Covariate-adjusted odds ratios between sotrovimab treatment and the severe outcome was conducted using logistic regression. RESULTS: There were 22,289 individuals meeting the treatment criteria for sotrovimab. There were 1603 treated and 6299 untreated individuals included in the analysis. The outcome occurrence in the study was 5.49% (treated) and 4.21% (untreated), with a median time from diagnosis to treatment of 1.00 days (interquartile range 2.00 days). In the propensity-matched cohort, sotrovimab was not associated with lower odds of a severe outcome (odds ratio 1.20, 95% confidence interval 0.91-1.58), adjusting for confounding variables. CONCLUSIONS: After adjusting for confounding variables, sotrovimab treatment was not associated with lower odds of a severe outcome within 30-days of COVID-19-positive date.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".