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Record W4394211965 · doi:10.6084/m9.figshare.18849551

Novel <i>RCBTB1</i> variants causing later-onset non-syndromic retinal dystrophy with macular chorioretinal atrophy

2022· dataset· en· W4394211965 on OpenAlexaboutno aff
Andrew J. Catomeris, Brian G. Ballios, Riccardo Sangermano, Naomi E. Wagner, Jason Comander, Eric A. Pierce, Emily Place, Kinga M. Bujakowska, Rachel M. Huckfeldt

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMacular dystrophyOphthalmologyMedicineAtrophyRetinalInternal medicine

Abstract

fetched live from OpenAlex

Variants in <i>RCBTB1</i> were recently described to cause a retinal dystrophy with only eight families described to date and a predominant phenotype of macular atrophy and peripheral reticular degeneration. Here, we further evaluate the genotypic and phenotypic characteristics of biallelic <i>RCBTB1</i>-associated retinal dystrophy in a North American clinic population. A retrospective analysis of genetic and clinical features was performed in individuals with biallelic variants in <i>RCBTB1</i>. Three unrelated individuals of French-Canadian descent with rare biallelic <i>RCBTB1</i> variants were identified. All individuals shared a novel p.(Ser342Leu) missense variant; one patient was homozygous whereas the other two each possessed a second unique novel variant p.(Gln120*) and p.(Pro224Leu). All three had macula-predominant disease with symptom onset in the fifth decade of life. This report adds to the genetic diversity of <i>RCBTB1</i>-associated disease. These cases confirm the later-onset, relative to many other retinal dystrophies, and macular focus of disease described in most cases to-date. They are thus a reminder of considering hereditary disease in the differential for later-onset macular atrophy.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.161
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1610.000

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 head0.011
GPT teacher head0.223
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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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