Bibliographic record
Abstract
Ranging from aplastic uterus (including Mayer-Rokitansky-Kuster-Hauser syndrome) to incomplete septate uterus, uterine malformations as a group are relatively frequent in the general population. Specific causes remain largely unknown. Although most occurrences ostensibly seem sporadic, familial recurrences have been observed, which strongly implicate genetic factors. Through the study of animal models, human syndromes, and structural chromosomal variation, several candidate genes have been proposed and subsequently tested with targeted methods in series of individuals with isolated, non-isolated, or syndromic uterine malformations. To date, a few genes have garnered strong evidence of causality, mainly in syndromic presentations (HNF1B, WNT4, WNT7A, HOXA13). Sequencing of candidate genes in series of individuals with isolated uterine abnormalities has been able to suggest an association for several genes, but confirmation of a strong causative effect is still lacking for the majority of them. We review the current state of knowledge about the developmental origins of uterine malformations, with a focus on the genetic variants that have been implicated or associated with these conditions in humans, and we discuss potential reasons for the high rate of negative results. The evidence for various environmental and epigenetic factors is also reviewed. © 2016 Wiley Periodicals, Inc.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".