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Record W6892268202 · doi:10.5061/dryad.stqjq2bzw

Five-years of ocrelizumab in relapsing multiple sclerosis: OPERA studies open-label extension

2020· dataset· en· W6892268202 on OpenAlexaff

Bibliographic record

VenueDRYAD · 2020
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaMcGill University
Fundersnot available
KeywordsOcrelizumabAdverse effectClinical trialLesionOpera

Abstract

fetched live from OpenAlex

Objective To assess over 3 years of follow-up, the effects of maintaining or switching to ocrelizumab (OCR) therapy on clinical and MRI outcomes and safety measures in the open-label extension (OLE) phase of the pooled OPERA studies in relapsing multiple sclerosis. Methods After 2 years of double-blind, controlled treatment, patients continued OCR (600 mg infusions every 24 weeks) or switched from interferon (IFN) β-1a (44 μg 3 times weekly) to OCR when entering the OLE phase (3 years). Adjusted annualized relapse rate, time to onset of 24-week confirmed disability progression/improvement (CDP/CDI), brain MRI activity (gadolinium-enhanced and new/enlarging T2 lesions), and percentage brain volume change were analyzed. Results Of patients entering the OLE phase, 88.6% completed Year 5. The cumulative proportion with 24-week CDP was lower in patients who initiated OCR earlier, vs patients initially receiving IFN β-1a (16.1% vs 21.3% at Year 5; p=0.014). Patients continuing OCR maintained, and those switching from IFN β-1a to OCR attained near complete and sustained suppression of new brain MRI lesion activity from Year 3 to 5. Over the OLE phase, patients continuing OCR exhibited less whole brain volume loss from double-blind study baseline vs those switching from IFN β-1a (–1.87% vs –2.15% at Year 5; p<0.01). Adverse events were consistent with past reports and no new safety signals emerged with prolonged treatment. Conclusion Compared with patients switching from IFN β-1a, earlier and continuous OCR treatment up to 5 years provided sustained benefit on clinical and MRI measures of disease progression.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.021
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.006

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 head0.173
GPT teacher head0.354
Teacher spread0.181 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2020
Admission routes1
Has abstractyes

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