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Record W4394675816 · doi:10.1016/j.ajoc.2024.102051

Multimodal imaging of white preretinal lesions in atypical familial exudative vitreoretinopathy: Case report and literature review

2024· article· en· W4394675816 on OpenAlexafffund
Liam Redden, Douglas S.M. Iaboni, Sarah van der Ende, Mathew Nightingale, Daniel Gaston, Christopher R. McMaster, Johane M. Robitaille, R. Rishi Gupta

Bibliographic record

VenueAmerican Journal of Ophthalmology Case Reports · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsMedicineOphthalmologyProliferative vitreoretinopathyRetinalDermatologyRetinal detachment

Abstract

fetched live from OpenAlex

Purpose: To report a rare clinical finding of preretinal granules associated with atypical familial exudative vitreoretinopathy (FEVR) and perform a review of the literature. Observations: An asymptomatic 18-year-old male was referred for unilateral peripheral avascular retina evaluation in association with presumed FEVR. He was first noted to have white preretinal granules on fundus examination at five years of age. The lesions remained unchanged over the subsequent years. Genetic testing did not reveal a pathogenic or likely pathogenic variant in a known FEVR gene. A review of the literature revealed five other cases of FEVR with similar findings. Conclusions and Importance: Literature review suggests preretinal granules may present rarely in FEVR. Negative genetic screening of known FEVR genes in our patient with atypical FEVR suggests either a molecularly distinct etiology supporting the rarity of this association with FEVR or, alternatively, the presence of granules in developmental retinal vascular anomalies that are not specific to FEVR. Future study and genetic testing is necessary to better understand the cause of these preretinal granules and the clinical manifestations of FEVR.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.007
GPT teacher head0.292
Teacher spread0.285 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations0
Published2024
Admission routes2
Has abstractyes

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