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Record W2915004215 · doi:10.3148/cjdpr-2018-044

Adverse Birth Outcomes Associated with Types of Eating Disorders: A Review

2019· review· en· W2915004215 on OpenAlexaffvenue
Kimberly D. Charbonneau, Jamie A. Seabrook

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2019
Typereview
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsChildren’s Health Research Institute
Fundersnot available
KeywordsMedicinePregnancyAnorexia nervosaBody mass indexBulimia nervosaEating disordersMiscarriageObstetricsBirth weightPremature birthOdds ratioLow birth weightGestational ageAdverse effectBinge eatingPediatricsPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

At least 5% of women have an eating disorder (ED) during pregnancy. These EDs affect prepregnancy body mass index (BMI) and weight gain during pregnancy, factors associated with birth complications and adverse neonatal outcomes. This review contributes to the literature by examining several adverse birth outcomes associated with EDs and differentiates between past and present EDs. Of the 18 articles reviewed, EDs were associated with preterm birth in 5/14 (36%) and small-for-gestational-age in 5/8 (63%) studies. Anorexia Nervosa increases the odds of a low birth weight baby, particularly when women enter pregnancy with a low BMI. Binge Eating Disorder is positively associated with having a large-for-gestational-age infant, and Bulimia Nervosa is associated with miscarriage when symptomatic during pregnancy. Having a current ED increases the risk for adverse birth outcomes more than a past ED. Since the aetiology of adverse birth outcomes is multi-factorial, drawing conclusions about causal relationships between EDs and birth outcomes is problematic given the small number of studies reporting these outcomes. Resources should target preconception interventions that put EDs into remission and help women achieve a healthier BMI prior to pregnancy, as these have been consistently shown to improve birth outcomes.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.945
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.159
GPT teacher head0.478
Teacher spread0.318 · 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 designOther design
Domainnot available
GenreReview

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

Citations30
Published2019
Admission routes2
Has abstractyes

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