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Record W4379966734 · doi:10.1093/cid/ciad356

T-SPOT.TB Reactivity in Southern African Children With and Without <i>in Utero</i> Human Immunodeficiency Virus Exposure

2023· article· en· W4379966734 on OpenAlexafffund
Saori C. Iwase, Paul T. Edlefsen, Lynnette Bhebhe, Kesego Motsumi, Sikhulile Moyo, Anna‐Ursula Happel, Danica Shao, Nicholas Mmasa, Sara R. Schenkel, Melanie Gasper, Melanie Dubois, Megan Files, Chetan Seshadri, Fergal J. Duffy, John D. Aitchison, Mihai G. Netea, Jennifer Jao, D. William Cameron, Clive M. Gray, Heather B. Jaspan, Kathleen M. Powis

Bibliographic record

VenueClinical Infectious Diseases · 2023
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Allergy and Infectious DiseasesNational Institute of Child Health and Human DevelopmentCanadian Institutes of Health ResearchNational Institute of Diabetes and Digestive and Kidney DiseasesInternational Development Research CentreYoshida Scholarship FoundationCanadian International Development Agency
KeywordsMedicineIn uteroTuberculosisHuman immunodeficiency virus (HIV)VirologyViral diseaseSidaImmunologyTransmission (telecommunications)VirusPregnancyBiologyPathologyFetus

Abstract

fetched live from OpenAlex

Infants who are human immunodeficiency virus (HIV)-exposed uninfected (iHEU) experience higher risk of infectious morbidity than infants HIV-unexposed uninfected (iHUU). We compared tuberculosis (TB) infection prevalence in 418 Bacillus Calmette-Guérin vaccinated sub-Saharan African iHEU and iHUU aged 9-18 months using T-SPOT.TB. Prevalence of TB infection was low and did not differ by HIV exposure status.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.043
GPT teacher head0.383
Teacher spread0.340 · 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 designObservational
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

Citations2
Published2023
Admission routes2
Has abstractyes

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