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Record W2587052911 · doi:10.1093/jpepsy/jsw095

Tobacco Exposure and Conditional Weight-for-Length Gain by 2 Years of Age

2016· article· en· W2587052911 on OpenAlexaff
Danielle S. Molnar, Diana Rancourt, Robert C. Schlauch, Xiaozhong Wen, Marilyn A. Huestis, Rina D. Eiden

Bibliographic record

VenueJournal of Pediatric Psychology · 2016
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsBrock University
FundersNational Institute on Drug AbuseNational Institute on Alcohol Abuse and AlcoholismNational Institutes of Health
KeywordsMedicinePregnancyWeight gainBirth weightNicotineObesityMeconiumProspective cohort studyPhysiologyPediatricsFetusInternal medicineBody weightBiology

Abstract

fetched live from OpenAlex

Objective: To prospectively examine dose-response and timing effects of prenatal (PTE) and postnatal tobacco exposure on obesity risk assessed by conditional weight-for-length gain (CWFLG), by 2 years of age. CWFLG over the first 2 years of life was examined for 117 PTE and 57 nonexposed children. Repeated assessments of PTE were conducted beginning in the first trimester of pregnancy, using multiple methods. PTE or postnatal exposure status was not predictive of CWFLG. However, there was a dose-response association and an association with fetal exposure ascertained by infant meconium positive for nicotine and metabolites. PTE is related to restricted growth at birth, yet associated with accelerated CWFLG by 2 years of age, a measure that controls for birthweight differences. Results highlight the importance of examining dose-response and timing of exposure associations, along with the importance of obesity risk-reduction interventions within the first 2 years of life among PTE children.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.228

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 head0.024
GPT teacher head0.329
Teacher spread0.305 · 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
Published2016
Admission routes1
Has abstractyes

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