Learning from the COVID-19 Pandemic: Next-generation universal vaccines and immunotherapeutic research
Bibliographic record
Abstract
Learning from the COVID-19 Pandemic: Next-generation universal vaccines and immunotherapeutic research With the COVID-19 pandemic behind us, we need to focus on universal vaccines and/or immunotherapeutic strategies and technologies to tackle ongoing endemic infections with SARS-CoV2, influenza, and RSV and prepare for any future pandemics, says Dr Babita Agrawal. In the 21st century, we have witnessed the emergence of respiratory infections with pandemic potential, like corona and influenza viruses, on multiple occasions. Due to the global dissemination of one such coronavirus, SARS-CoV2 (severe acute respiratory syndrome-coronavirus type-2), the World Health Organization (WHO) declared a worldwide pandemic in March 2020. The global public health emergency was declared over in May 2023 by the WHO, but infections with variants of SARS-CoV2 continue to evolve and cause infections worldwide. (1) Besides public health measures, developing, approving, and implementing vaccines against SARS-CoV2 have helped mitigate and end the pandemic. However, the existing vaccines against SARS-CoV2 are not preventive, do not induce mucosal immunity, induce only short-term protection and are ineffective against emerging variants, thereby requiring regular updated boosters.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".