Genetic and Antigenic Typing of Seasonal Influenza Virus Breakthrough Cases from a 2008-2009 Vaccine Efficacy Trial
Bibliographic record
Abstract
Estimations of the effectiveness of vaccines against seasonal influenza virus are guided by comparisons of the antigenicities between influenza virus isolates from clinical breakthrough cases with strains included in a vaccine. This study examined whether the prediction of antigenicity using a sequence analysis of the hemagglutinin (HA) gene-encoded HA1 domain is a simpler alternative to using the conventional hemagglutination inhibition (HI) assay, which requires influenza virus culturing. Specimens were taken from breakthrough cases that occurred in a trivalent influenza virus vaccine efficacy trial involving >43,000 participants during the 2008-2009 season. A total of 498 influenza viruses were successfully subtyped as A(H3N2) (380 viruses), A(H1N1) (29 viruses), B(Yamagata) (23 viruses), and B(Victoria) (66 viruses) from 603 PCR- or culture-confirmed specimens. Unlike the B strains, most A(H3N2) (377 viruses) and all A(H1N1) viruses were classified as homologous to the respective vaccine strains based on their HA1 domain nucleic acid sequence. HI titers relative to the respective vaccine strains and PCR subtyping were determined for 48% (182/380) of A(H3N2) and 86% (25/29) of A(H1N1) viruses. Eighty-four percent of the A(H3N2) and A(H1N1) viruses classified as homologous by sequence were matched to the respective vaccine strains by HI testing. However, these homologous A(H3N2) and A(H1N1) viruses displayed a wide range of relative HI titers. Therefore, although PCR is a sensitive diagnostic method for confirming influenza virus cases, HA1 sequence analysis appeared to be of limited value in accurately predicting antigenicity; hence, it may be inappropriate to classify clinical specimens as homologous or heterologous to the vaccine strain for estimating vaccine efficacy in a prospective clinical trial.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".