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Record W2053615666 · doi:10.1177/1352458512447597

Evaluation of pattern-reversal visual evoked potential in patients with neuromyelitis optica

2012· article· en· W2053615666 on OpenAlexaff
Silvio Pessanha Neto, Regina Maria Papais Alvarenga, Cláudia Cristina Ferreira Vasconcelos, Luiz Carlos Pinto

Bibliographic record

VenueMultiple Sclerosis Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsCytodiagnostics (Canada)
Fundersnot available
KeywordsNeuromyelitis opticaMultiple sclerosisOptic neuritisVisual evoked potentialsMedicineEvoked potentialTransverse myelitisAudiologySpinal cordOphthalmologyImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: The visual evoked potential (VEP) is used in the evaluation of multiple sclerosis (MS) patients, showing a delay in P100 wave latency with no changes in amplitude in 60-100% of cases. In the last decade, the recurrent form of neuromyelitis optica (NMO) has been recognized, and clinically characterized by acute events of transverse myelitis (TM) and optic neuritis (ON), differing from MS in clinical and laboratory criteria. Despite these differences, so far, the VEP parameters described in MS have been used in the evaluation of patients with NMO. The objective of this study was to investigate VEP responses in NMO. METHODS: Patients with NMO underwent pattern-reversal visual stimulation. Nineteen patients were selected for the study. RESULTS: Among the 38 eyes examined, 18 (47.4%) had no visual evoked responses and 13 (34.2%) had a reduction of P100 wave amplitude with normal latency. Only two (5.3%) had the pattern described in MS and five (13.2%) were normal. CONCLUSION: Evaluation of VEP in patients with definite NMO revealed a pattern that is different from that of MS in 81.6% of eyes examined, characterized by the absence of responses, or decreased amplitude with normal latency.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.089
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.079
GPT teacher head0.312
Teacher spread0.233 · 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

Citations45
Published2012
Admission routes1
Has abstractyes

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