Celiac Disease and Maternal Infertility and Pregnancy Outcomes: Is Screening Necessary?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".