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Record W2336013912 · doi:10.1016/j.ajoc.2016.04.003

A case study of choroideremia carrier – Use of multi-spectral imaging in highlighting clinical features

2016· article· en· W2336013912 on OpenAlexaff
Pravin U. Dugel, Cheryl Zimmer, Ayda M. Shahidi

Bibliographic record

VenueAmerican Journal of Ophthalmology Case Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsAnnidis (Canada)
Fundersnot available
KeywordsChoroideremiaMedicineOphthalmologyRetinal

Abstract

fetched live from OpenAlex

PURPOSE: To report the use of non-invasive multi-spectral imaging of a female choroideremia (CHM) carrier with mild visual symptoms and extensive fundus mottling. OBSERVATION: This was an observational case report study. A symptomatic 42-year-old female with a history of binocular CHM presented for routine ocular examination and underwent review of her clinical and photographic records, optical coherence tomography (OCT), intravenous fluorescein angiography (IVFA) and multi-spectral imaging (MSI). Dilated fundus examination and photography revealed similar outcomes of diffuse mottling with normal looking vessels. IVFA showed large irregular and confluent patches of RPE atrophy in the peripapillary and parapapillary areas as well as the midperiphery, corresponding to the OCT findings. The entire range of MSI imaging (520-940 nm) clearly illustrated the anomalies of the fundus including retinal pigment epithelium (RPE) mottling with melanin clumping not readily seen with the other imaging modalities. MSI fundus autofluorescence (MSI-FAF) showed a spotty hypo and hyperautofluorescent appearance of the fundus, consistent with the observations seen on IVFA and OCT images. CONCLUSION AND IMPORTANCE: MSI significantly improves visualization of the retinal pigment epithelium in choroideremia. The non-invasive nature of MSI technique is a valuable tool in monitoring the effect of retinal and choroidal presentation in patients with CHM.

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 score0.091
Threshold uncertainty score0.508

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 head0.060
GPT teacher head0.394
Teacher spread0.334 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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