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Record W2189631957 · doi:10.1186/s40942-015-0023-6

Multimodal imaging of combined hamartoma of the retina and retinal pigment epithelium associated with an acquired vitelliform lesion

2015· article· en· W2189631957 on OpenAlexaff
Bora Chae, Elona Dhrami‐Gavazi, Kunal K. Dansingani, K. Bailey Freund, Winston Lee, Lawrence A. Yannuzzi

Bibliographic record

VenueInternational Journal of Retina and Vitreous · 2015
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsColumbia College
Fundersnot available
KeywordsRetinal pigment epitheliumHamartomaLesionEpiretinal membraneRetinaOphthalmologyMedicineRetinalOptical coherence tomographyOptic nervePathologyVisual acuityBiologyNeuroscienceVitrectomy

Abstract

fetched live from OpenAlex

BACKGROUND: We present a case of a combined hamartoma of the retina and retinal pigment epithelium associated with a subfoveal acquired vitelliform lesion induced by vitreomacular traction. The purpose of this report is to present a unifying hypothesis of these concurrent findings, as aided by multimodal imaging. CASE PRESENTATION: A 25-year-old white man presented with a 6-month history of a visual disturbance in his left eye. At presentation, ophthalmic assessment showed a combined hamartoma adjacent to his optic nerve that had caused marked corrugation within the inner retinal surface. An acquired vitelliform lesion was present in the macula with an associated epiretinal membrane as demonstrated on spectral-domain optical coherence tomography. Optical coherence tomography angiography corroborated the clinical diagnosis of combined hamartoma. CONCLUSIONS: We are not aware of previous cases of a combined hamartoma associated with an acquired vitelliform lesion. As previously proposed in acquired vitelliform lesions related to epiretinal membrane and vitreoretinal traction, we believe that macular tractional forces might interfere with retinal pigment epithelium phagocytosis of shed outer segments, leading to the occurrence of this acquired vitelliform lesion.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.017
GPT teacher head0.290
Teacher spread0.273 · 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 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

Citations17
Published2015
Admission routes1
Has abstractyes

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