MétaCan
Menu
Back to cohort
Record W4286861211

Not All Cases of Visual Snows are Benign: Mimics of Visual Snow Syndrome

2021· article· en· W4286861211 on OpenAlexaboutno aff
C Hang, L Leishangthem

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2021
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsnot available
Fundersnot available
KeywordsSnowMedicineGeologyGeomorphology
DOInot available

Abstract

fetched live from OpenAlex

Chenyue Hang,1 Lakshmi Leishangthem,2 Yan Yan3 1Ottawa-Shanghai Joint School of Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 2Department of Neurology and Ophthalmology, University of Connecticut, Farmington, CT, USA; 3Department of Ophthalmology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of ChinaCorrespondence: Yan YanDepartment of Ophthalmology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Pujian Road 160, Shanghai, People’s Republic of ChinaTel +86 21 58752345Email hz2004yan@gmail.comAbstract: Visual snow syndrome (VSS) is a clinical disorder characterized by pan-field visual disturbance. It is a diagnosis of exclusion since its pathophysiology remains unknown. Excluding other mimics is of great significance since some serious pathologies can have secondary visual snow (VS) as an initial presentation. Delayed or incorrect diagnosis of these VSS mimics may lead to permanent vision loss or even death. The purpose of this review is to help physicians distinguish VSS mimics promptly to avoid bad outcomes. The authors performed a PubMed literature search of articles, case reports, and reviews describing VS symptoms in patients with underlying diseases other than VSS. The red flags of secondary VS symptoms were highlighted, such as new-onset or intermittent VS, unilateral or quadrant VS, and accompanied ocular or neurological deficits. There are four main categories of VSS mimics, ie, including neurological disorders, ocular pathologies, drug-related VS, and other systemic diseases. The physicians could largely exclude most etiologies based on history taking, ophthalmologic and neurologic examinations, and neuroimaging. Further research in VS should carefully define and unify the inclusion and exclusion criteria of this disorder and investigate these secondary VS conditions and their pathogenesis.Keywords: birdshot chorioretinopathy, Creutzfeldt–Jakob disease, hallucinogen persisting perception disorder, migraine with visual aura, visual snow

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.000

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 head0.259
GPT teacher head0.555
Teacher spread0.296 · 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 designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)Same topicSkin Protection and AgingFrench-language works237,207