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Record W2107569441 · doi:10.1177/1352458508101939

Proof of concept studies for tissue-protective agents in multiple sclerosis

2009· review· en· W2107569441 on OpenAlexaff
LR Mehta, S. R. Schwid, Douglas L. Arnold, GR Cutter, Shreeram Aradhye, LJ Balcer, Peter A. Calabresi, JA Cohen, PE Cole, Robert Glanzman, Susan Goelz, Matilde Inglese, Ravish Kapoor, Ludwig Kappos, Rivka Kreitman, FD Lublin, Arielle Mann, Ruth Ann Marrie, P O'Looney, CH Polman, BM Ravina, Stephen C. Reingold, J Richert, AW Sandrock, Emmanuelle Waubant

Bibliographic record

VenueMultiple Sclerosis Journal · 2009
Typereview
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Manitoba
FundersNational Institute of Neurological Disorders and Stroke
KeywordsMultiple sclerosisPlaceboMedicineClinical trialClinical study designProof of conceptClinical endpointSample size determinationRandomized controlled trialTreatment effectPhysical medicine and rehabilitationIntensive care medicineComputer sciencePathologyAlternative medicineStatisticsPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: There is considerable interest in tissue-protective treatments for multiple sclerosis (MS). METHODS AND OBJECTIVES: We convened a group of MS clinical trialists and related researchers to discuss designs for proof of concept studies utilizing currently available data and assessment methods. RESULTS: Our favored design was a randomized, double-blind, parallel-group study of active treatment versus placebo focusing on changes in brain volume from a post-baseline scan (3-6 months after starting treatment) to the final visit 1 year later. Study designs aimed at reducing residual deficits following acute exacerbations are less straightforward, depending greatly on the anticipated rapidity of treatment effect onset. CONCLUSIONS: The next step would be to perform one or more studies of potential tissue-protective agents with these designs in mind, creating the longitudinal data necessary to refine endpoint selection, eligibility criteria, and sample size estimates for future trials.

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)0.002
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.472
GPT teacher head0.436
Teacher spread0.036 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations9
Published2009
Admission routes1
Has abstractyes

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