Early Real-World Data to Assess Benefits and Risks of COVID-19 Vaccines: A Systematic Review of Methods
Bibliographic record
Abstract
Since the authorization of the first COVID-19 vaccines in December 2020, multiple studies using real-world data (RWD) have been published to assess their effectiveness/safety profile. This systematic review aimed to characterize the methods and outcomes of studies using RWD for assessment of COVID-19 vaccines, four months after vaccine approval. MEDLINE and EMBASE were searched to identify published studies until 6 May 2021. Two independent researchers selected relevant publications and extracted data from included studies. The risk of bias was assessed using New-Castle Ottawa tools. After screening 1086 studies, 15 were included. Out of the 15 studies, 12 (80%) followed a cohort design, 8 (53%) were based on USA data, 7 (47%) assessed health care professionals, and 14 articles (93%) assessed the BNT162b2 vaccine. Data sources included institutional databases, electronic health records, and patient-generated data. The primary endpoint mainly described was SARS-CoV-2-infection. Hospitalization and mortality were assessed in 2 studies. For the comparability domain, six studies (40%) had a high risk of bias. A few months after the beginning of COVID-19 vaccination, Real-world Evidence (RWE) provided timely safety surveillance and comparative effectiveness with findings that showed similar findings to Randomized control trial (RCT). Most of the initiatives assessed BNT162b2 and were conducted in the USA and used healthcare workers' data.
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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 | 0.042 | 0.135 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".