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Record W2598384273 · doi:10.1159/000455216

Early-Life Antibiotic Exposure, Gut Microbiota Development, and Predisposition to Obesity

2017· review· en· W2598384273 on OpenAlexaff
Meghan B. Azad, Shirin Moossavi, Arthur H. Owora, Shadi Sepehri

Bibliographic record

VenueNestlé Nutrition Institute Workshop series · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsChildren's Hospital Research Institute of ManitobaUniversity of Manitoba
Fundersnot available
KeywordsGut floraObesityAntibioticsBiologyMedicineImmunologyMicrobiologyEndocrinology

Abstract

fetched live from OpenAlex

Antibiotics are often prescribed inappropriately to infants and young children, with potentially adverse effects on the developing gut microbiota and related metabolic processes. We review evidence from 17 epidemiologic studies suggesting that antibiotic exposure during critical periods of early development may influence weight gain and the development of obesity. Complementary research in both humans and rodents indicates that gut microbiota play a key role in this process, although further research is needed to confirm and characterize the causal mechanisms involved. Obesity is a complex and multifactorial condition; thus, a multipronged prevention strategy will be required to curb the current obesity epidemic. Evidence to date suggests this strategy should include the judicious use of antibiotics, especially in early life when the developing gut microbiota is particularly susceptible to perturbations with long-lasting implications for metabolic programming and obesity risk.

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
Bibliometrics0.0020.002
Science and technology studies0.0000.000
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 head0.062
GPT teacher head0.336
Teacher spread0.275 · 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

Citations41
Published2017
Admission routes1
Has abstractyes

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