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Record W4318191253 · doi:10.1177/13524585221149886

Should trigeminal neuralgia be considered a clinically isolated syndrome?

2023· article· en· W4318191253 on OpenAlexafffund
Sarasa Tohyama, Jiwon Oh, Makenna Timm, Joshua C. Cheng, Aisha Halawani, David J. Mikulis, Andrew Solomon, Mojgan Hodaie

Bibliographic record

VenueMultiple Sclerosis Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicTrigeminal Neuralgia and Treatments
Canadian institutionsSt. Michael's HospitalToronto Western HospitalUniversity of TorontoUniversity Health Network
FundersInstitute of Neurosciences, Mental Health and AddictionCanadian Institutes of Health ResearchBiogenBristol-Myers Squibb
KeywordsMedicineTrigeminal neuralgiaMultiple sclerosisMagnetic resonance imagingConfusionClinically isolated syndromeLesionDermatologyRadiologyPediatricsSurgeryPsychiatryPsychology

Abstract

fetched live from OpenAlex

The association between trigeminal neuralgia (TN) and multiple sclerosis (MS) is well established. Many MS patients with TN have magnetic resonance imaging (MRI) evidence of a symptomatic demyelinating lesion. Although infratentorial presentations are included in the diagnostic criteria for MS, there remains confusion in clinical practice as to whether TN should be considered a clinically isolated syndrome for the application of McDonald criteria. In this case series, we discuss this diagnostic quandary in patients presenting with TN and additional MRI findings suggestive of MS and highlight the unmet need for data in such patients to optimally guide their care.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.232
GPT teacher head0.348
Teacher spread0.116 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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