Influenza vaccine effectiveness in older adults: study methods, transparency and impacts on public health
Bibliographic record
Abstract
We re-read the article by Domnich et al. [1] following citation in a recently-published ECDC report [2]. Domnich et al. conducted a post-hoc, test-negative case-control design (TND) study of Italian hospitalizations (using DRIVE [3] data) and estimated relative effectiveness (rVE) of 59.2% for adjuvanted vs unadjuvanted influenza vaccination, which was influential in ECDC conclusions. These results directionally contradict Italian hospitalized results from DRIVE itself which reported lower adjusted VE for adjuvanted than non-adjuvanted vaccine in both 2018/19 (34% [−12–61] for adjuvanted vs 92% [40–99] for Fluarix Tetra) [4] and 2019/2020 (52% [27–68] vs 67% [8–88]) seasons, findings associated with negative, rather than positive, adjuvanted rVE [5].
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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 | 0.234 | 0.507 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier 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".