The Impact of Adjuvanted Influenza Vaccine on Disease Severity in the US: A Stochastic Model
Bibliographic record
Abstract
Influenza can exacerbate underlying medical conditions. In this study, we modelled the potential impact of an egg-based quadrivalent influenza vaccine (QIVe) or adjuvanted QIV (aQIV) on hospitalizations and mortality from influenza-related cardiovascular disease (CVD), respiratory, and other complications in adults ≥65 years of age in the US with underlying chronic conditions. We used a stochastic decision-tree model, with 1000 simulations varying input across predicted ranges. Due to the variable nature of influenza across seasons and differences in published estimates for input parameters, data are presented as 95% confidence intervals. Compared with no vaccination, use of aQIV would prevent 135,450-564,360 hospitalizations and 1612-29,226 deaths across outcomes evaluated. Overall, aQIV prevented 1071-18,388 more hospitalizations and 85-1944 more deaths than QIVe. By routine seasonal vaccination against influenza, a substantial number of severe influenza-associated complications and deaths, caused by direct influenza symptoms or by exacerbation of chronic conditions, can be prevented in high-risk adults ≥65 years of age in the US.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".