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Record W2118372228 · doi:10.1191/1352458502ms769oa

International consensus statement on the use of disease-modifying agents in multiple sclerosis

2002· article· en· W2118372228 on OpenAlexaff
M. S. Freedman, L. D. Blumhardt, Bruno Brochet, Gıancarlo Comı, J H Noseworthy, Magnhild Sandberg‐Wollheim, Per Soelberg Sørensen

Bibliographic record

VenueMultiple Sclerosis Journal · 2002
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsStatement (logic)Multiple sclerosisVotingConsensus conferenceMedicineFamily medicineDiseaseSteering committeeAlternative medicineMEDLINEPolitical sciencePsychiatryPathologyInternal medicineLawEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: To provide recommendations on the use of disease-modifying agents in the management of multiple sclerosis (MS) and to ensure that treatment will be available to those patients who may benefit. METHODS: An initial draft of the consensus statement was prepared by the Steering Committee and amended in the light of written comments from a group of MS specialists. At a subsequent workshop, the wording of the consensus statement was discussed, modified if necessary, and the participants indicated their level of support using an electronic voting system. A new draft of the statement was then sent to a much larger group of international opinion leaders in MS for further comment. RESULTS: A number of statements were agreed, which outline the criteria for consideration of disease-modifying therapy for MS and recommendations for treatment. Each statement was accepted completely, or with only minor reservations by 95% or more of those present at the workshop. CONCLUSIONS: Periodic reviews and modifications to the statement will be required, as new approaches to the treatment of MS and other therapeutic agents become available.

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)0.007
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.515
GPT teacher head0.356
Teacher spread0.159 · 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

Citations42
Published2002
Admission routes1
Has abstractyes

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