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Record W4407192154 · doi:10.1016/j.tim.2024.12.012

Diversity in women and their vaginal microbiota

2025· review· en· W4407192154 on OpenAlexaff
Sandra Condori-Catachura, Sarah Ahannach, Mónica Ticlla, Josiane Kenfack, Esemu Livo, Kingsley C. Anukam, Viviana Pinedo-Cancino, María Carmen Collado, Maria Gloria Domínguez-Bello, Corrie Miller, Gabriel Vinderola, Sonja Merten, Gilbert Donders, Thies Gehrmann, Sarah Lebeer

Bibliographic record

VenueTrends in Microbiology · 2025
Typereview
Languageen
FieldImmunology and Microbiology
TopicReproductive tract infections research
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of New Brunswick
FundersH2020 European Research CouncilBiocodex Microbiota FoundationUniversiteit AntwerpenNational Institutes of HealthVLIRUOSNational Institute of General Medical SciencesEngineering Research CentersMinisterio de Ciencia e InnovaciónFonds Wetenschappelijk OnderzoekAgencia Estatal de InvestigaciónFondation MérieuxMinisterio de Ciencia, Innovación y Universidades
KeywordsBiologyDiversity (politics)MicrobiomeVaginal floraEvolutionary biologyBacterial vaginosisEcologyComputational biologyMicrobiologyGenetics

Abstract

fetched live from OpenAlex

Women's health is essential to global societal and economic wellbeing, yet health disparities remain prevalent. The vaginal microbiota plays a critical role in health, with research indicating that reduced levels of core bacteria, such as lactobacilli, are associated with conditions like bacterial vaginosis (BV) and increased infection susceptibility. Lower levels of vaginal lactobacilli are reported more frequently in women of African and Latin American descent compared with women of European and Asian descent. However, geographical and other study inclusion and analysis biases influence current research. This opinion highlights the need for a more comprehensive understanding of a 'healthy' vaginal microbiome. It underscores efforts to broaden global research on microbiome diversity in socially relevant contexts, avoiding inappropriate applications of terms such as race and ethnicity.

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 score0.012

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 science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.046
GPT teacher head0.346
Teacher spread0.300 · 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

Citations26
Published2025
Admission routes1
Has abstractyes

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Same venueTrends in MicrobiologySame topicReproductive tract infections researchFrench-language works237,207