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Record W2114942144 · doi:10.3109/14767058.2012.688080

The effect of maternal Class III obesity on neonatal outcomes: a retrospective matched cohort study

2012· article· en· W2114942144 on OpenAlexaff
Laura Gaudet, Xiaowen Tu, Deshayne B. Fell, Darine El‐Chaâr, Shi Wu Wen, Mark Walker

Bibliographic record

VenueThe Journal of Maternal-Fetal & Neonatal Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineObstetricsOdds ratioRetrospective cohort studyBody mass indexGestational ageObesityBreastfeedingConfidence intervalCohort studyPediatricsPregnancyCohortBirth weightInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To compare outcomes of neonates born from women with Class III obesity with those whose mothers were of normal body weight. METHODS: A retrospective cohort study of live-born singleton infants was undertaken. Maternal prepregnancy body mass index (BMI) defined matched normal and Class III obese cohorts. Multivariable regression models were used to determine adjusted relative odds ratios (aOR) and 95% confidence intervals (CI) for selected adverse neonatal outcomes. RESULTS: Newborns exposed to maternal Class III obesity had greater risks of fetal overgrowth and low cord artery pH. Class III obesity was protective against small for gestational age and low birthweight. There was no difference in the risk of preterm delivery, meconium in the amniotic fluid or breastfeeding initiation. CONCLUSIONS: The new knowledge generated by this study provides further information on unique challenges faced by newborns of women with Class III obesity, suggesting more specialized care in the intrapartum and neonatal periods may be beneficial.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.297
Teacher spread0.288 · 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

Citations18
Published2012
Admission routes1
Has abstractyes

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