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Breastfeeding and obesity at 14 years: A cohort study

2006· article· en· W2110747414 on OpenAlexaff
Linda Shields, Michael O’Callaghan, Gail Williams, Jake M. Najman, William Bor

Bibliographic record

VenueJournal of Paediatrics and Child Health · 2006
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsInstitute of Population and Public Health
FundersNational Medical Research CouncilNational Health and Medical Research Council
KeywordsBreastfeedingMedicineOverweightConfoundingObesityDemographyLogistic regressionCohort studyCohortPediatricsBirth weightLongitudinal studyGestational agePregnancyInternal medicine

Abstract

fetched live from OpenAlex

AIM: To determine the influence of breastfeeding on overweight and obesity in early adolescence. METHODS: Data about breastfeeding duration, BMI of children at 14 years, and confounding variables, were collected from an ongoing longitudinal study of a birth cohort of 7776 children in Brisbane. Prevalence of overweight and obesity at 14 years was assessed according to duration of breastfeeding, with logistic regression being used to adjust for the influence of confounders. RESULTS: Data were available for 3698 children, and those not included were significantly different in age, educational level, income, race, birthweight, and small-for-gestational-age status. Breastfeeding for longer than six months was protective of obesity (OR 0.6, 95% CI 0.4, 0.96) though not of overweight. When confounding variables were considered the effect size diminished and lost statistical significance OR 0.8 (95% CI 0.5, 1.3). Breastfeeding for less than 6 months had no effect on either obesity or overweight though a trend was found for increased prevalence of overweight at 14 years with shorter periods of breastfeeding. CONCLUSION: This investigation contributes to the gathering body of evidence that breastfeeding for longer than 6 months has a modest protective effect against obesity in adolescence.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.279
Teacher spread0.268 · 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

Citations76
Published2006
Admission routes1
Has abstractyes

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