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Record W1820841059 · doi:10.1111/ceo.12152

Genomics and anterior segment dysgenesis: a review

2013· review· en· W1820841059 on OpenAlexafffund
Yoko Itō, Michael A. Walter

Bibliographic record

VenueClinical and Experimental Ophthalmology · 2013
Typereview
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsDysgenesisAnterior Eye SegmentTrabecular meshworkGlaucomaMedicineIRIS (biosensor)GeneticsBioinformaticsAnatomyCorneaBiologyOphthalmologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Anterior segment dysgenesis refers to a spectrum of disorders affecting structures in the anterior segment of the eye including the iris, cornea and trabecular meshwork. Approximately 50% of patients with anterior segment dysgenesis develop glaucoma. Traditional genetic methods using linkage analysis and family-based studies have identified numerous disease-causing genes such as PAX6, FOXC1 and PITX2. Despite these advances, phenotypic and genotypic heterogeneity pose continuing challenges to understand the mechanisms underlying the complexity of anterior segment dysgenesis disorders. Genomic methods, such as genome-wide association studies, are potentially an effective tool to understand anterior segment dysgenesis and the individual susceptibility to the development of glaucoma. In this review, we provide the rationale, as well as the challenges, to utilizing genomic methods to examine anterior segment dysgenesis disorders.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.102
GPT teacher head0.443
Teacher spread0.341 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations120
Published2013
Admission routes2
Has abstractyes

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