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Record W3194537927 · doi:10.1111/ijpo.12847

Influence of antibiotics given during labour and birth on body mass index <i>z</i> scores in children in the All Our Families pregnancy cohort

2021· article· en· W3194537927 on OpenAlexaff
Teja Klančič, Amanda M. Black, Raylene A. Reimer

Bibliographic record

VenuePediatric Obesity · 2021
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineBody mass indexObesityConfoundingPregnancyCohortPediatricsStandard scoreObstetricsBirth weightCohort studyDemographyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND/OBJECTIVES: Little is known about obesity risk associated with intrapartum antibiotic prophylaxis (IAP). Our objective was to determine if maternal antibiotic exposure during birth is associated with child body mass index (BMI) z scores in the first 3 years of life. METHODS: In 2008 to 2010, 3388 pregnant women were recruited to the All Our Families study. Here, we included women with available data from obstetrical records on antibiotic use during birth (n = 1303) and children with at least one valid BMI z score (final sample n = 1262). The primary outcome was infant BMI z score at 1, 2 and 3 years of age. RESULTS: IAP occurred in 432 of 1262 women. Children exposed to IAP had significantly higher mean [standard error (SE)] BMI z scores (1.071 [0.087] unit) at 1 year of age compared to non-exposed infants (0.744 [0.064] unit). Although the association was no longer significant after adjustment for confounding factors in the growth trajectory model, IAP resulted in a 0.255 unit increase in BMI z score at 1 year of age. Differences in BMI z score between exposed and non-exposed at baseline (year 1) only remained significant in sensitivity analysis. CONCLUSION: The potential association between maternal IAP and increased infant BMI z score at 1 year of age should be confirmed in other cohorts and warrants investigation of interventions to mitigate this possible 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 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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.247
Teacher spread0.241 · 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 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

Citations8
Published2021
Admission routes1
Has abstractyes

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