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Record W3030389606 · doi:10.1212/wnl.0000000000009636

The 2013 clinical course descriptors for multiple sclerosis

2020· review· en· W3030389606 on OpenAlexaff
Fred Lublin, Timothy Coetzee, Jeffrey A. Cohen, Ruth Ann Marrie, Alan J. Thompson

Bibliographic record

VenueNeurology · 2020
Typereview
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMultiple sclerosisDiseaseMedicineFraming (construction)PsychologyIntensive care medicinePathologyPsychiatryGeography

Abstract

fetched live from OpenAlex

The clinical courses of multiple sclerosis were defined in 1996 and refined in 2013 to provide a time-based assessment of the current status of the individual. These definitions have been successfully used by clinicians, clinical trialists, and regulatory authorities. Recent regulatory decisions produced variations and discrepancies in the use of the clinical course descriptions. We provide here a clarification of the concepts underlying these descriptions and restate the principles used in their development. Importantly, we highlight the critical importance of time framing the disease course modifiers activity and progression and clarify the difference between the terms worsening and progressing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0090.008
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.003

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 head0.372
GPT teacher head0.453
Teacher spread0.080 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations123
Published2020
Admission routes1
Has abstractyes

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