MétaCan
Menu
Back to cohort
Record W2801082739 · doi:10.1002/jnr.24245

Relationship between neural crest cell specification and rare ocular diseases

2018· review· en· W2801082739 on OpenAlexafffund
Monica Akula, Jeong Won Park, Judith A. West‐Mays

Bibliographic record

VenueJournal of Neuroscience Research · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicOcular Disorders and Treatments
Canadian institutionsMcMaster University
FundersCore Research for Evolutional Science and TechnologyNational Institutes of HealthNational Eye InstituteMcMaster University
KeywordsNeural crestPAX6Eye developmentMesenchymeBiologyNeuroscienceProgenitor cellMesenchymal stem cellCell migrationCell biologyAnatomyCellGeneStem cellGeneticsPhenotypeTranscription factor

Abstract

fetched live from OpenAlex

Development of the eye is closely associated with neural crest cell migration and specification. Eye development is extremely complex, as it requires the working of a combination of local factors, receptors, inductors, and signaling interactions between tissues such as the optic cup and periocular mesenchyme (POM). The POM is comprised of neural crest-derived mesenchymal progenitor cells that give rise to numerous important ocular structures including those tissues that form the optic cup and anterior segment of the eye. A number of genes are involved in the migration and specification of the POM such as PITX2, PITX3, FOXC1, FOXE3, PAX6, LMX1B, GPR48, TFAP2A, and TFAP2B. In this review, we will discuss the relevance of these genes in the development of the POM and how mutations and defects result in rare ocular diseases.

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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.244
GPT teacher head0.457
Teacher spread0.213 · 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

Citations46
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Neuroscience ResearchSame topicOcular Disorders and TreatmentsFrench-language works237,207