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A multicenter, open label, single-arm, phase 3b study (CONSONANCE) to assess efficacy of ocrelizumab in patients with primary and secondary progressive multiple sclerosis: year 1 interim analysis of cognition outcomes (P1-1.Virtual)

2022· article· en· W4389404873 on OpenAlexaff
Ralph Benedict, Maria Pia Sormani, Marisa McGinley, Douglas L. Arnold, Amit Bar‐Or, Robert A Bermel, Pavan Bhargava, Declan Chard, Guy Gherardi, Roland Henry, Owain W. Howell, Christine Lebrun‐Frénay, Letizia Leocani, Catherine Lubetzki, Agne Kazlauskaite, Thomas Kuenzel, Xavier Montalbán, Finn Sellebjerg, Gıancarlo Comı, Licínio Craveiro, Helmut Butzkeuven

Bibliographic record

VenueNeurology · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMcGill UniversityNeuroRx Research (Canada)
Fundersnot available
KeywordsOcrelizumabMedicineOpen labelInterim analysisMultiple sclerosisInterimMulticenter studyClinical endpointRelapsing remittingInternal medicineClinical trialRandomized controlled trialRituximabImmunology

Abstract

fetched live from OpenAlex

To report year 1 interim analysis of cognitive outcomes in the single-arm, phase 3b CONSONANCE study (NCT03523858) designed to evaluate effectiveness and safety of ocrelizumab in patients with primary (PPMS) or secondary progressive multiple sclerosis (SPMS).

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)0.006
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.116
GPT teacher head0.365
Teacher spread0.249 · 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 designNon-randomized trial
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
Published2022
Admission routes1
Has abstractyes

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