MétaCan
Menu
Back to cohort
Record W4385716588 · doi:10.1093/infdis/jiad322

Sequence Data From a Travel-Associated Case of Microcephaly Highlight a Persisting Risk due to Zika Virus Circulation in Thailand

2023· article· en· W4385716588 on OpenAlexfundno aff
Solène Marquine, Guillaume André Durand, Gabriela Modenesi, Siham Khouadhria, Géraldine Piorkowski, Cyril Badaut, Thomas Canivez, Xavier de Lamballerie, Gilda Grard, Raphaëlle Klitting

Bibliographic record

VenueThe Journal of Infectious Diseases · 2023
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsnot available
FundersMinistère de la Santé et des Services sociauxInstitut National de la Santé et de la Recherche MédicaleInstitut de Recherche pour le DéveloppementAix-Marseille UniversitéService de Santé des Armées
KeywordsZika virusMicrocephalyTimelineIncidence (geometry)PregnancyMedicineVirologyPediatricsVirusBiologyGeographyGenetics

Abstract

fetched live from OpenAlex

Zika virus has been circulating in Thailand since 2002 through continuous but likely low-level circulation. Here, we describe an infection in a pregnant woman who traveled to Thailand and South America during her pregnancy. By combining phylogenetic analysis with the patient's travel history and her pregnancy timeline, we confirmed that she likely got infected in Thailand at the end of 2021. This imported case of microcephaly highlights that Zika virus circulation in the country still constitutes a health risk, even in a year of lower incidence. MAIN POINTS: Here we trace the origin of travel-acquired microcephaly to Thailand, providing additional evidence that pre-American lineages of Zika virus can harm the fetus and highlighting that Zika virus constitutes a health threat even in a year of lower incidence.

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)0.000
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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.030
GPT teacher head0.298
Teacher spread0.268 · 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 designCase report
Domainnot available
GenreEmpirical

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

Citations7
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Infectious DiseasesSame topicMosquito-borne diseases and controlFrench-language works237,207