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Record W2779511327 · doi:10.18192/uojm.v7i2.2194

Celiac Disease and Maternal Infertility and Pregnancy Outcomes: Is Screening Necessary?

2017· article· en· W2779511327 on OpenAlexaffvenue
Syed Mohammad, Kai Yi Wu, Johnny W. Huang

Bibliographic record

VenueUniversity of Ottawa Journal of Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicCeliac Disease Research and Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineGynecologyInfertilityIntestinal malabsorptionPregnancyCoeliac diseaseDiseaseUnexplained infertilityObstetricsInternal medicineBiology

Abstract

fetched live from OpenAlex

Celiac disease is a common autoimmune condition that is often underappreciated in pregnant women. Due to the difficulty in conducting high-quality studies involving pregnant patients, the evidence supporting the association between celiac disease and maternal fertility and pregnancy outcome, and the benefits of screening for celiac disease in this population are unclear. Therefore, we sought to review the relevant literature to gain a better understanding of the impact of celiac disease on maternal fertility and fetal outcome. Our findings suggest a role for celiac screening in women with unexplained infertility. RésuméLa maladie cœliaque est une maladie auto-immune commune qui est souvent sous-estimée chez les femmes enceintes. En raison de la difficulté à mener des études de haute qualité impliquant des patientes enceintes, les preuves soutenant l’association entre la maladie cœliaque et la fertilité maternelle et l’issue de la grossesse, et les avantages du dépistage de la maladie cœliaque dans cette population ne sont pas claires. Par conséquent, nous avons cherché à examiner la littérature pertinente pour mieux comprendre l’impact de la maladie cœliaque sur la fertilité maternelle et l’issue fœtale. Nos résultats suggèrent un rôle pour le dépistage de la maladie cœliaque chez les femmes présentant une infertilité inexpliquée.

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 score0.030
Threshold uncertainty score0.327

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 studies0.0000.001
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 head0.026
GPT teacher head0.295
Teacher spread0.269 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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