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Record W2809911284 · doi:10.1093/femsre/fuy018

Antibiotics in early life: dysbiosis and the damage done

2018· review· en· W2809911284 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFEMS Microbiology Reviews · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
FundersCanadian Institutes of Health ResearchAzrieli FoundationIsrael Science FoundationCanadian HIV Trials Network, Canadian Institutes of Health ResearchInternational Development Research Centre
KeywordsAntibioticsDysbiosisBiologyGut floraMicrobiomeImmunityImmunologyAllergyAdverse effectImmune systemMicrobiologyBioinformaticsPharmacology

Abstract

fetched live from OpenAlex

Antibiotics are the most common type of medication prescribed to children, including infants, in the Western world. While use of antibiotics has transformed previously lethal infections into relatively minor diseases, antibiotic treatments can have adverse effects as well. It has been shown in children, adults and animal models that antibiotics dramatically alter the gut microbial composition. Since the gut microbiota plays crucial roles in immunity, metabolism and endocrinology, the effects of antibiotics on the microbiota may lead to further health complications. In this review, we present an overview of the effects of antibiotics on the microbiome in children, and correlate them to long-lasting complications of obesity, behavior, allergies, autoimmunity and other diseases.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.737
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.322
Teacher spread0.286 · 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