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Record W4290830384 · doi:10.1136/jnnp-2022-abn2.232

188 SUNFISH 3-year efficacy and safety of risdiplam in types 2 and 3 SMA

2022· article· en· W4290830384 on OpenAlexaff
Laurent Servais, Elena Mazzone, A. Nascimento, Maryam Oskoui, Giovanni Baranello, Marianne Gerber, Carmen Martín, Wai Yin Yeung, Eugenio Mercuri

Bibliographic record

VenueJournal of Neurology Neurosurgery & Psychiatry · 2022
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsSMA*MedicinePlaceboSpinal muscular atrophyPopulationClinical trialMotor functionPhysical therapyInternal medicinePediatricsPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Risdiplam (EVRYSDI®) is an oral survival of motor neuron 2 (SMN2) premRNA splicing modifier approved by the EMA and MHRA for the treatment of patients aged ≥2 months, with a clinical diagnosis of Type 1, 2 or 3 spinal muscular atrophy (SMA) or 1–4 copies of SMN2. SUNFISH ( NCT02908685 ) is a two-part, randomised, placebocontrolled, double-blind study in a broad population of patients aged 2–25 years with Type 2/3 SMA. Part 2 assesses the efficacy and safety of the Part 1-selected dose of risdiplam versus placebo in Type 2 and non-ambulant Type 3 SMA. Participants were treated with risdiplam or placebo for 12 months; all participants then received risdiplam until Month 24. At Month 24, patients were offered the opportunity to enter the openlabel extension. The primary outcome of Part 2 – change from baseline to Month 12 in the 32-item Motor Function Measure total score in patients treated with risdiplam (n=120) versus placebo (n=60) – was met. Gains observed with risdiplam at Month 12 were maintained or improved upon at Month 24. At Month 24, there were no treatment-related safety findings leading to withdrawal. Here we present efficacy and safety data of patients who have received risdiplam for 36 months.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.267
Teacher spread0.256 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2022
Admission routes1
Has abstractyes

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