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Record W2598784521 · doi:10.1177/2474126417697593

West Nile Virus Chorioretinitis With Foveal Involvement

2017· article· en· W2598784521 on OpenAlexaff
Gary Yau, Eric K. Chin, D. Wilkin Parke, Steven R. Bennett, David R.P. Almeida

Bibliographic record

VenueJournal of VitreoRetinal Diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsQueen's University
Fundersnot available
KeywordsChorioretinitisFovealMedicineLesionOphthalmologyOptical coherence tomographyVisual acuityRetinalRetinaPathologyBiology

Abstract

fetched live from OpenAlex

Purpose: To describe the clinical course of foveal West Nile virus (WNV) chorioretinitis with longitudinal spectral domain optical coherence tomography (SD-OCT) imaging. Methods: Case report. Results: A 41-year-old man with diabetes mellitus presented with flashes and floaters of both eyes (OU) and decreased vision of the right eye (OD) 2 weeks after being discharged from a local hospital. He had been treated for WNV meningoencephalitis, and he recovered systemically with supportive therapy. Ophthalmic examination revealed WNV chorioretinitis bilaterally, with predominantly foveal involvement OD. His best-corrected visual acuity (BCVA) was 8/200 OD and 20/20 of the left eye (OS). Spectral domain optical coherence tomography revealed 2 distinct lesion types—the “classic” outer retinal lesion and an intraretinal lesion. Both lesions had associated disruption of the normal outer hyperreflective retinal layers on SD-OCT. Longitudinal SD-OCT over the ensuing 6 weeks revealed a gradual reconstitution of these layers, with BCVA concurrently improving to 20/40 OD. Conclusion: We describe the consecutive findings seen on SD-OCT of retinal lesions in WNV chorioretinitis. The tomographic natural history of these lesions involved reconstitution of OCT deficits, with corresponding improvement in functional visual status.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.011
GPT teacher head0.276
Teacher spread0.266 · 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 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

Citations2
Published2017
Admission routes1
Has abstractyes

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