Vaccine efficacy against SARS-CoV-2 for Pfizer BioNTech, Moderna, and AstraZeneca vaccines: a systematic review
Bibliographic record
Abstract
The purpose of this systematic review was to report on the vaccine efficacy (VE) of three SARS-CoV-2 vaccines approved by Health Canada: Pfizer BioNTech, Moderna, and AstraZeneca. Four databases were searched for primary publications on population-level VE. Ninety-two publications matched the inclusion criteria, and the extracted data were separated by vaccine type: mRNA vaccines (Pfizer and Moderna) and the AstraZeneca vaccine. The median VE for PCR-positive patients and various levels of clinical disease was determined for the first and second doses of both vaccine types against multiple SARS-CoV-2 variants. The median VE for PCR-positive infections against unidentified variants from an mRNA vaccine was 64.5 and 89%, respectively, after one or two doses. The median VE for PCR-positive infections against unidentified variants from the AstraZeneca vaccine was 53.4 and 69.6%, respectively, after one or two doses. The median VE for two doses of mRNA for asymptomatic, symptomatic, and severe infection against unidentified variants was 85.5, 93.2, and 92.2%, respectively. The median VE for two doses of AstraZeneca for asymptomatic, symptomatic, and severe infection against unidentified variants was 69.7, 71, and 90.2%, respectively. Vaccine efficacy numerically increased from the first to the second dose, increased from the first 2 weeks to the second 2 weeks post-vaccination for both doses, but decreased after 4 months from the second dose. Vaccine efficacy did not differ by person's age.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".