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Record W2120980877 · doi:10.1101/cshperspect.a025825

A Review of Secondary Photoreceptor Degenerations in Systemic Disease

2014· review· en· W2120980877 on OpenAlexafffund
Naveen Mysore, Jamie Koenekoop, Shen Li, Huanan Ren, Vafa Keser, Irma López-Solache, Robert K. Koenekoop

Bibliographic record

VenueCold Spring Harbor Perspectives in Medicine · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsMontreal Children's HospitalMcGill University Health CentreMcGill UniversityMcGill Genome Centre
FundersNational Eye InstituteCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsRetinal degenerationRetinalRetinaRetinitis pigmentosaSystemic diseaseBlindnessDegeneration (medical)BiologyDiseaseMedicinePathologyNeuroscienceOphthalmologyOptometry

Abstract

fetched live from OpenAlex

Photoreceptor neuronal degenerations are common and incurable causes of human blindness with one in 2000 affected. Approximately, half of all patients are associated with known mutations in more than 200 disease genes. Most retinal degenerations are restricted to the retina (primary retinal degeneration) but photoreceptor degeneration can also be found in a wide variety of systemic and syndromic diseases. These are called secondary retinal degenerations. We review several well-known systemic diseases with retinal degenerations (RD). We discuss RD with hearing loss, RD with brain disease, and RD with musculoskeletal disease. We then postulate which retinal degenerations may also have previously undetected systemic features. Emerging new and exciting evidence is showing that ubiquitously expressed genes associated with multitissue syndromic disorders may also harbor mutations that cause isolated primary retinal degeneration. Examples are RPGR, CEP290, CLN3, MFSD5, and HK1 mutations that cause a wide variety of primary retinal degenerations with intact systems.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.776
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.020
GPT teacher head0.316
Teacher spread0.297 · 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 designSystematic review
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

Citations16
Published2014
Admission routes2
Has abstractyes

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