Development and implementation of a potential coronavirus disease 2019 (COVID-19) vaccine: A systematic review and meta-analysis of vaccine clinical trials
Bibliographic record
Abstract
BACKGROUND: To date, there is no comprehensive systematic review and meta-analysis to assess the suitability of COVID-19 vaccines for mass immunization. The current systematic review and meta-analysis was conducted to evaluate the safety and immunogenicity of novel COVID-19 vaccine candidates under clinical trial evaluation and present a contemporary update on the development and implementation of a potential vaccines. METHODS: For this study PubMed, MEDLINE, and Embase electronic databases were used to search for eligible studies on the interface between novel coronavirus and vaccine design until December 31, 2020. RESULTS: We have included fourteen non-randomized and randomized controlled phase I-III trials. Implementation of a universal vaccination program with proven safety and efficacy through robust clinical evaluation is the long-term goal for preventing COVID-19. The immunization program must be cost-effective for mass production and accessibility. Despite pioneering techniques for the fast-track development of the vaccine in the current global emergency, mass production and availability of an effective COVID-19 vaccine could take some more time. CONCLUSION: Our findings suggest a revisiting of the reported solicited and unsolicited systemic adverse events for COVID-19 candidate vaccines. Hence, it is alarming to judiciously expose thousands of participants to COVID-19 candidate vaccines at Phase-3 trials that have adverse events and insufficient evidence on safety and effectiveness that necessitates further justification.
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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 | 0.024 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".