Preclinical Safety Assessment of Vaccines Developed Against COVID-19
Bibliographic record
Abstract
SARS-Cov-2 or Covid-19 is a novel coronavirus that originated from Wuhan, China, in December 2019 and has caused serious and sometimes lethal respiratory infections globally. This highly contagious virus spreads by nasal droplets or aerosol particles. Since its origin, many people have been infected and died in many countries, especially China, India, United States, Canada, Brazil, Turkey, Europe, Iran, and South Africa. The spread of this virus is still continuing in several developed and developing countries despite imposed lockdowns and curfews as well as social distancing practices recommended by healthcare specialists. Unfortunately, no specific antiviral drug or alternative therapy is available for treating Covid-19 infected patients. Many unprecedented and fast-track approaches have been used to produce a wide array of vaccines for mass vaccination in a matter of months, and several vaccines are still undergoing clinical trials for new variants. More than 300 vaccinations have been developed worldwide, and nine of them have been approved by drug regulatory authorities for mass vaccination in many countries. The effectiveness claims of different vaccines range from 50% to 95%. The aims and objectives of this chapter are to highlight the results of preclinical safety evaluation of different vaccines developed against Covid-19, and to gain insights regarding the long-term safety and effectiveness of vaccines that are cost-effective and affordable for mass vaccination.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".