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Record W1970799201 · doi:10.1007/s00439-013-1381-5

Next generation sequencing-based molecular diagnosis of retinitis pigmentosa: identification of a novel genotype-phenotype correlation and clinical refinements

2013· article· en· W1970799201 on OpenAlexaff
Feng Wang, Hui Wang, Han-Fang Tuan, Duy H. Nguyen, Vincent Sun, Vafa Keser, Sara J. Bowne, Lori S. Sullivan, Hongrong Luo, Ling Zhao, Xia Wang, Jacques Zaneveld, Jason S. Salvo, Sorath Noorani Siddiqui, Louise Mao, Dianna K. Wheaton, David G. Birch, Kari Branham, John R. Heckenlively, Cindy Wen, Ken Flagg, Henry Ferreyra, Jacqueline Pei, Ayesha Khan, Huanan Ren, Keqing Wang, Irma López, Raheel Qamar, Juan Carlos Zenteno, Raúl Ayala-Ramírez, Beatriz Buentello‐Volante, Qing Fu, David Simpson, Yumei Li, Ruifang Sui, Giuliana Silvestri, Stephen P. Daiger, Robert K. Koenekoop, Kang Zhang, Rui Chen

Bibliographic record

VenueHuman Genetics · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsMcGill University Health Centre
FundersU.S. National Library of MedicineNational Institute of Environmental Health SciencesNational Center for Research ResourcesNational Institute of General Medical SciencesNational Eye InstituteNational Heart, Lung, and Blood InstituteNational Institutes of HealthFoundation Fighting Blindness
KeywordsRetinitis pigmentosaProbandBiologyStargardt diseaseGeneticsABCA4GenotypeGenetic counselingLocus heterogeneityRetinal degenerationDiseaseHuman geneticsGenetic testingAlleleMedical geneticsGenetic heterogeneityRetinal DisorderGenotype-phenotype distinctionPhenotypeGeneMutationRetinalPathologyMedicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.302
Teacher spread0.241 · 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 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

Citations242
Published2013
Admission routes1
Has abstractno

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