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Record W4401437625 · doi:10.7759/cureus.66405

Unilateral Vogt-Koyanagi-Harada Disease With Two Distinct Choroidal Neovascular Membranes: A Case Report

2024· article· en· W4401437625 on OpenAlexaff
Raeba Mathew, Boopathy Murugavel

Bibliographic record

VenueCureus · 2024
Typearticle
Languageen
FieldMedicine
TopicOcular Diseases and Behçet’s Syndrome
Canadian institutionsASTER
Fundersnot available
KeywordsMedicineVogt–Koyanagi–Harada diseaseUveitisComplicationDexamethasoneDiseaseChoroidal neovascularizationOphthalmologyVisual acuityNeovascularizationSystemic diseaseSurgeryEye diseaseDermatologyPathologyInternal medicineAngiogenesis

Abstract

fetched live from OpenAlex

Vogt-Koyanagi-Harada (VKH) disease is a multisystem inflammatory disease that usually presents with bilateral panuveitis. Unilateral manifestations of VKH, albeit rare, have been described. Choroidal neovascularization may occur as a complication during the disease. Reactivation of uveitis may also occur during treatment. A patient with unilateral features of VKH disease presented with two distinct types of choroidal neovascular membranes and two episodes of reactivation of posterior uveitis. He underwent treatment with a combination of systemic steroids/immunosuppressive agents, intravitreal anti-vascular endothelial growth factor (anti-VEGF) agents, and dexamethasone implants with good clinical response. Patients with VKH disease need to be diagnosed early, treated adequately with a combination of systemic and ocular medication, and followed up diligently for any complication that may arise, to optimize visual acuity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0060.003
Science and technology studies0.0040.004
Scholarly communication0.0030.005
Open science0.0020.006
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.275
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), 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 routes1
Has abstractyes

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