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Record W4366352768 · doi:10.1128/mbio.00491-23

Maternal Bacterial Engraftment in Multiple Body Sites of Cesarean Section Born Neonates after Vaginal Seeding—a Randomized Controlled Trial

2023· article· en· W4366352768 on OpenAlexafffund
Noel T. Mueller, Moira K. Differding, Haipeng Sun, Jincheng Wang, Shira Levy, Varsha Deopujari, Lawrence J. Appel, Martin J. Blaser, Tanima Kundu, Ankit A. Shah, Maria Gloria Domínguez-Bello, Suchitra K. Hourigan

Bibliographic record

VenuemBio · 2023
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsCanadian Institute for Advanced Research
FundersNational Heart, Lung, and Blood InstituteNIH Clinical CenterNational Institutes of HealthJohns Hopkins Bloomberg School of Public HealthInova Health SystemNational Institute of Allergy and Infectious DiseasesQuest DiagnosticsJohns Hopkins UniversityRutgers, The State University of New JerseyCanadian Institute for Advanced Research
KeywordsImmune systemRandomized controlled trialMedicineSeedingColonizationElective cesarean sectionPhysiologyBiologyImmunologyPregnancySurgeryMicrobiology

Abstract

fetched live from OpenAlex

Children delivered by elective C-section are not exposed to the birth canal and show altered microbiota development. Impairing microbial colonization during early life alters metabolic and immune programming and is associated with an increased risk of immune and metabolic diseases. In a double-blind, randomized, placebo-controlled trial, we determined the effect of vaginal seeding on the skin and stool microbiota of elective C-section born neonates and found that vaginal seeding increased mother-to-neonate microbiota transmission and caused compositional changes and a reduction in the skin and stool microbiota diversity. The reduction of neonatal skin and stool microbiota diversity when maternal vaginal microbiota is provided is intriguing and highlights the need of larger randomized studies to determine the ecological mechanisms and effects of vaginal seeding on clinical outcomes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.013
GPT teacher head0.259
Teacher spread0.246 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations41
Published2023
Admission routes2
Has abstractyes

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