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Record W4402581495 · doi:10.1001/jamaneurol.2024.3062

Differential Diagnosis of Suspected Multiple Sclerosis in Pediatric and Late-Onset Populations

2024· article· en· W4402581495 on OpenAlexaff
Le H. Hua, Andrew Solomon, Sílvia Tenembaum, Antonio Scalfari, Àlex Rovira, Kevin Rostásy, Scott D. Newsome, Ruth Ann Marrie, Melinda Magyari, Orhun H. Kantarci, Bernhard Hemmer, Cheryl Hemingway, Mary Pat Harnegie, Jennifer Graves, Jeffrey A. Cohen, Riley Bove, Brenda Banwell, John R. Corboy, Emmanuelle Waubant

Bibliographic record

VenueJAMA Neurology · 2024
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMultiple sclerosisDifferential diagnosisMedicinePediatricsPathologyPsychiatry

Abstract

fetched live from OpenAlex

Importance: While the typical onset of multiple sclerosis (MS) occurs in early adulthood, 2% to 10% of cases initially present prior to age 18 years, and approximately 5% after age 50 years. Guidance on approaches to differential diagnosis in suspected MS specific to these 2 age groups is needed. Observations: There are unique biological factors in children younger than 18 years and in adults older than age 50 years compared to typical adult-onset MS. These biological differences, particularly immunological and hormonal, may influence the clinical presentation of MS, resilience to neuronal injury, and differential diagnosis. While mimics of MS at the typical age at onset have been described, a comprehensive approach focused on the younger and older ends of the age spectrum has not been previously published. Conclusions and Relevance: An international committee of MS experts in pediatric and adult MS was formed to provide consensus guidance on diagnostic approaches and key clinical and paraclinical red flags for non-MS diagnosis in children and older adults.

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)0.001
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 score0.006
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

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

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 head0.073
GPT teacher head0.312
Teacher spread0.239 · 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

Citations15
Published2024
Admission routes1
Has abstractyes

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