MétaCan
Menu
Back to cohort
Record W2412487272 · doi:10.1002/ajmg.a.37775

Etiologies of uterine malformations

2016· review· en· W2412487272 on OpenAlexaff
Adeline Jacquinet, Debra Millar, Anna Lehman

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2016
Typereview
Languageen
FieldMedicine
TopicGynecological conditions and treatments
Canadian institutionsChild and Family Research InstituteUniversity of British Columbia
Fundersnot available
KeywordsBiologyCandidate geneEtiologyPopulationHNF1BGeneticsGeneEpigeneticsMedicinePathologyHomeobox

Abstract

fetched live from OpenAlex

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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)0.0020.000

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 head0.071
GPT teacher head0.428
Teacher spread0.358 · 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.

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

Citations66
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Medical Genetics Part ASame topicGynecological conditions and treatmentsFrench-language works237,207