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Record W2907950729 · doi:10.4049/jimmunol.1801149

Original Antigenic Sin: How First Exposure Shapes Lifelong Anti–Influenza Virus Immune Responses

2019· review· en· W2907950729 on OpenAlexafffund
Ali Zhang, Hannah D. Stacey, Caitlin E. Mullarkey, Matthew S. Miller

Bibliographic record

VenueThe Journal of Immunology · 2019
Typereview
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
FundersCanadian Institutes of Health Research
KeywordsImmune systemVirologyOriginal antigenic sinAntigenVirusImmunologyBiologyInfluenza A virusAntigenic drift

Abstract

fetched live from OpenAlex

The term "original antigenic sin" (OAS) was first used in the 1960s to describe how one's first exposure to influenza virus shapes the outcome of subsequent exposures to antigenically related strains. In the decades that have passed, OAS-like responses have been shown to play an integral role in both protection from and susceptibility to infections. OAS may also have an important deterministic role in the differential efficacy of influenza vaccine responses observed for various age cohorts across seasons. In this article, we review how the understanding of OAS has progressed from its initial description and highlight important outstanding questions in need of further study.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.152
GPT teacher head0.413
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations205
Published2019
Admission routes2
Has abstractyes

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