Frontrunners in the race to develop a SARS-CoV-2 vaccine
Bibliographic record
Abstract
Numerous studies continue to be published on the COVID-19 pandemic that is being caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Given the rapidly evolving global response to SARS-CoV-2, here we primarily review the leading COVID-19 vaccine strategies that are currently in Phase III clinical trials. Nonreplicating viral vector strategies, inactivated virus, recombinant protein subunit vaccines, and nucleic acid vaccine platforms are all being pursued in an effort to combat the infection. Preclinical and clinal trial results of these efforts are examined as well as the characteristics of each vaccine strategy from the humoral and cellular immune responses they stimulate, effects of any adjuvants used, and the potential risks associated with immunization such as antibody-dependent enhancement. A number of promising advancements have been made toward the development of multiple vaccine candidates. Preliminary data now emerging from phase III clinical trials show encouraging results for the protective efficacy and safety of at least 3 frontrunning candidates. There is hope that one or more will emerge as potent weapons to protect against SARS-CoV-2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".