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Record W4394602078 · doi:10.1212/wnl.0000000000204912

Safety of Remibrutinib Across Immune-mediated Diseases Supports Development in Multiple Sclerosis (P8-6.015)

2024· article· en· W4394602078 on OpenAlexaff
Bernd C. Kieseier, Xavier Montalbán, Mitzi Williams, Laura Airas, Sarbjit S. Saini, Michihiro Hide, Gordon Sussman, Jin Nakahara, Robert Bermel, Thomas Doerner, Brett Loop, Virginia DeLasHeras, Roman Willi, Sibylle Haemmerle, Artem Zharkov, Nathalie Barbier, Amin Azmon, Richard W. Siegel, Bruno Cenni, Heinz Wiendl, Marcus Maurer, Ana M. Giménez‐Arnau, Tanuja Chitnis

Bibliographic record

VenueNeurology · 2024
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMultiple sclerosisImmune systemMedicineNeuroscienceImmunologyPsychology

Abstract

fetched live from OpenAlex

To report the integrated safety profile of remibrutinib using pooled data from completed phase 2 clinical trials in chronic spontaneous urticaria (CSU), Sjögren syndrome (SjS), and asthma, including long-term treatment up to 52 weeks.

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)0.010
metaresearch head score (Gemma)0.006
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 score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.035
GPT teacher head0.303
Teacher spread0.268 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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