Bibliographic record
Abstract
Seasonal influenza remains a major public health problem, responsible for hundreds of thousands of deaths every year, mostly of elderly people. Despite the wide availability of vaccines, there are multiple problems decreasing the effectiveness of vaccination programs. These include viral variability and hence the requirement to match strains by estimating which will become prevalent each season, problems associated with vaccine and adjuvant production, and the route of administration as well as the perceived lower vaccine efficiency in older adults. Clinical protection is still suboptimal for all of these reasons, and vaccine uptake remains too low in most countries. Efforts to improve the effectiveness of influenza vaccines include developing universal vaccines independent of the circulating strains in any particular season and stimulating cellular as well as humoral responses, especially in the elderly. This commentary assesses progress over the last 3 years towards achieving these aims. Since the beginning of 2020, an unprecedented international academic and industrial effort to develop effective vaccines against the new coronavirus SARS-CoV-2 has diverted attention away from influenza, but many of the lessons learned for the one will synergize with the other to mutual advantage. And, unlike the SARS-1 epidemic and, we hope, the SARS-CoV-2 pandemic, influenza will not be eliminated and thus efforts to improve influenza vaccines will remain of crucial importance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".