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Record W2520056820 · doi:10.1111/j.1755-3768.2016.0056

Genetics in microphthalmia

2016· article· en· W2520056820 on OpenAlexaff
Patrick Calvas, Erica E. Davis, Nicola Ragge, Lucas Fares‐Taie, Myriam Srour, Jacques L. Michaud, J.‐C. Kaplan, Jean‐Michel Rozet, Nicolas Chassaing

Bibliographic record

VenueActa Ophthalmologica · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicOcular Disorders and Treatments
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMicrophthalmiaAnophthalmiaBiologyGeneticsEye developmentSonic hedgehogHoloprosencephalyPhenotypeColobomaGeneEvolutionary biology

Abstract

fetched live from OpenAlex

Summary Congenital malformations of the eye comprise a wide spectrum of developmental defects. Anophthalmia‐microphthalmia (AM) is the most severe end of these conditions. The different ocular malformations are thought to be part of an overlapping spectrum of embryonic developmental defects. Phenotypic overlap is emphasized by molecular results demonstrating that the same genes may lead to variable defects. We aimed at delineating the molecular bases of AM in order to improve knowledge on eye development as well as patient care. We follow one of the largest cohort of ocular developmental defects cases with extensive clinical and genetic analyses and report here extensive genetics analysis delineating a valuable genetic epidemiology of AM triggering genes. To date a genetic cause is identified in less than half of the patients suffering AM. The most likely explanation for this is that only a small proportion of causative genes have been identified. That is, we report here the strategy used to identify novel AM genes among rare and mainly sporadic patients. This led highlighting the malformation spectrum of already known genes and delineating original pathways respectively involved in retinoic acid metabolism and Sonic Hedgehog signalling.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.391

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 studies0.0000.000
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 head0.015
GPT teacher head0.263
Teacher spread0.248 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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