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Record W3217773060 · doi:10.2217/nmt-2021-0042

MN-166 (ibudilast) in Amyotrophic Lateral Sclerosis in a Phase IIb/III Study: COMBAT-ALS Study Design

2021· article· en· W3217773060 on OpenAlexaff
Björn Oskarsson, Nicholas J. Maragakis, Richard Bedlack, Namita Goyal, Jenny Meyer, Angela Genge, Cynthia Bodkin, Samuel Maiser, Nathan P. Staff, Lorne Zinman, Nicholas Olney, John Turnbull, Benjamin Rix Brooks, Emelia Klonowski, Malath Makhay, Seiichi Yasui, Kazuko Matsuda

Bibliographic record

VenueNeurodegenerative Disease Management · 2021
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsMcMaster UniversityHealth Sciences CentreSunnybrook Health Science CentreMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsAmyotrophic lateral sclerosisTolerabilityMedicineNeuroprotectionRiluzoleQuality of life (healthcare)PlaceboClinical trialContext (archaeology)DiseasePharmacologyInternal medicineOncologyAdverse effectPathology

Abstract

fetched live from OpenAlex

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with motor neuron loss as a defining feature. Despite significant effort, therapeutic breakthroughs have been modest. MN-166 (ibudilast) has demonstrated neuroprotective action by various mechanisms: inhibition of proinflammatory cytokines and macrophage migration inhibitory factor, phosphodiesterase inhibition, and attenuation of glial cell activation in models of ALS. Early-phase studies suggest that MN-166 may improve survival outcomes and slow disease progression in patients with ALS. This article describes the rationale and design of COMBAT-ALS, an ongoing randomized, double-blind, placebo-controlled, multicenter Phase IIb/III study in ALS. This study is designed to evaluate the pharmacokinetics, safety and tolerability and assess the efficacy of MN-166 on function, muscle strength, quality of life and survival in ALS.

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)0.004
metaresearch head score (Gemma)0.001
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: Protocol · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.085
GPT teacher head0.345
Teacher spread0.259 · 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
GenreProtocol

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
Published2021
Admission routes1
Has abstractyes

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