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Record W43185941 · doi:10.1093/ofid/ofu052.236

370The Impact of Early Life Antibiotic Exposure on Childhood Weight Gain

2014· article· en· W43185941 on OpenAlexaff
Jeffrey S. Gerber, Elizabeth Prout, Rachael K. Ross, Matthew Bryan, Robert W. Grundmeier, Evanette Burrows, Carrie Daymont, Virginia Stallings, Theoklis Zaoutis

Bibliographic record

VenueOpen Forum Infectious Diseases · 2014
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineWeight gainAntibioticsEarly childhoodPediatricsGerontologyInternal medicineBody weightMicrobiologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Background. Antibiotic exposure has been shown to promote weight gain in livestock and has been associated with increased adiposity and altered metabolism in experimental animal models, mediated by alterations in the gut microbiome. Infancy and early childhood represent both an influential period of growth trajectory and a time during which antibiotic exposure is common and often inappropriate. Therefore, we sought to determine the impact of real world, early life antibiotic exposures on childhood weight gain. Methods. A longitudinal, retrospective study was conducted using a socioeconomically and racially diverse pediatric healthcare network serving > 200,000 children at 31 practices. We included children born between 2001 and 2011 who presented for a preventive health visit in the first 14 days of life and had at least 2 additional visits in the first year. We excluded children born < 35 weeks gestational age; birthweight <2,000 grams or below 5% for gestational age; with chronic medical conditions, prophylactic antibiotic use, or frequent steroid use. Exposures included all systemic antibiotic exposures in the first 6 months of life. The primary outcome was weight through age 8, standardized to WHO/CDC reference populations. A longitudinal mixed effects model was used to assess the association between standardized weight and age by antibiotic exposure interaction, adjusting for sex, race, insurance type, birthweight, preventive health care compliance, household size, birth year, baseline height and primary care practice site. A second analysis, including sets of twins where only one twin had early antibiotic exposure, used a longitudinal mixed effects model to assess the association between paired weight difference and age, adjusting for differences in sex, birthweight, and baseline height. Results. Of 38,756 children included in the analysis, 5,312 (13.7%) received antibiotics in the first 6 months of life. After adjustment for clinical and demographic variables, antibiotic exposure was associated with a decrease of 0.03 in weight z-score per year (p < 0.001). Of 47 sets of twins discordant in early antibiotic use, antibiotic exposure was not associated with a change in weight (p = 0.59). Conclusion. Using data from a large birth cohort, infant antibiotic exposure did not increase early childhood weight gain. Disclosures. T. Zaoutis, Merck: Investigator, Research grant Merck: Consultant, Consulting fee Pfizer: Consultant, Consulting fee Astellas: Consultant, Consulting fee

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)0.003
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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.272
Teacher spread0.263 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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