Efficacy and effectiveness of SARS-CoV-2 vaccines for death prevention: A protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: There is consistent evidence that SARS-CoV-2 vaccines have statistical and clinical significant efficacy to prevent incident and severe cases of COVID-19, although different outcomes were analyzed and different risk reductions were observed. However, randomized control trials (RCT) were not designed or powered to assess whether the vaccines prevent deaths, even though this was a secondary or exploratory outcome across many studies. Early real-world observational data suggest that these vaccines are highly effective in reducing hospitalization and all-cause mortality. Our objective is to summarize and appraise-the existing evidence on the efficacy and real-world effectiveness of all SARS-CoV-2 vaccines currently approved for full or limited use to prevent all-cause and COVID-19-attributed mortality. METHODS: The population consists of persons with a record of vaccination status and the outcome of interest. Randomized controlled trials, comparative cohort and case-control studies reporting vaccination with any of the vaccines approved (intervention) will be eligible. The primary outcome will be all cause deaths. COVID-19-attributed deaths and deaths attributable to the vaccination (adverse event deaths) will be secondary outcomes. We will compare deaths occurring in vaccinated persons versus those non-vaccinated or having received placebo. Studies in any language will be eligible. Two independent reviewers will screen for inclusion and assess quality of studies using the Cochrane Risk of Bias 2 and the ROBINS-1 tool, as appropriate. Hazard ratios will be calculated. Assessment of statistical heterogeneity amongst the studies will be done using I2 and prediction intervals, as well as visual inspection of the forest plots. Publication bias will be assessed using a funnel plot and Egger statistical test if we have more than 10 studies in a forest plot. We have followed the PRISMA-Protocol checklist for the current protocol, which is registered at Prospero (York University, CRD42021262211).
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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 | 0.059 | 0.108 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.023 | 0.032 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.051 | 0.005 |
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".