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Record W4380290922 · doi:10.1001/jamaneurol.2023.1625

Rituximab vs Ocrelizumab in Relapsing-Remitting Multiple Sclerosis

2023· article· en· W4380290922 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJAMA Neurology · 2023
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsCentre intégré de santé et de services sociaux de Chaudière-Appalaches
FundersNational Health and Medical Research CouncilEMD SeronoTrish Multiple Sclerosis Research FoundationDementia AustraliaTeva Pharmaceutical IndustriesRoyal Melbourne Hospital Neuroscience FoundationAlexion PharmaceuticalsMultiple Sclerosis AustraliaShionogiSanofiSanofi GenzymeUniversity of MelbourneAtara BiotherapeuticsMedical Research CouncilBiogenBristol-Myers Squibb
KeywordsOcrelizumabRelapsing remittingMultiple sclerosisRituximabMedicineDermatologyInternal medicineImmunologyLymphoma

Abstract

fetched live from OpenAlex

Importance: Ocrelizumab, a humanized monoclonal antibody targeted against CD20+ B cells, reduces the frequency of relapses by 46% and disability worsening by 40% compared with interferon beta 1a in relapsing-remitting multiple sclerosis (MS). Rituximab, a chimeric monoclonal anti-CD20 agent, is often prescribed as an off-label alternative to ocrelizumab. Objective: To evaluate whether the effectiveness of rituximab is noninferior to ocrelizumab in relapsing-remitting MS. Design, Setting, and Participants: This was an observational cohort study conducted between January 2015 and March 2021. Patients were included in the treatment group for the duration of study therapy and were recruited from the MSBase registry and Danish MS Registry (DMSR). Included patients had a history of relapsing-remitting MS treated with ocrelizumab or rituximab, a minimum 6 months of follow-up, and sufficient data to calculate the propensity score. Patients with comparable baseline characteristics were 1:6 matched with propensity score on age, sex, MS duration, disability (Expanded Disability Status Scale), prior relapse rate, prior therapy, disease activity (relapses, disability accumulation, or both), magnetic resonance imaging lesion burden (missing values imputed), and country. Exposure: Treatment with ocrelizumab or rituximab after 2015. Main outcomes and Measures: Noninferiority comparison of annualized rate of relapses (ARRs), with a prespecified noninferiority margin of 1.63 rate ratio. Secondary end points were relapse and 6-month confirmed disability accumulation in pairwise-censored groups. Results: Of the 6027 patients with MS who were treated with ocrelizumab or rituximab, a total of 1613 (mean [SD] age; 42.0 [10.8] years; 1089 female [68%]) fulfilled the inclusion criteria and were included in the analysis (898 MSBase, 715 DMSR). A total of 710 patients treated with ocrelizumab (414 MSBase, 296 DMSR) were matched with 186 patients treated with rituximab (110 MSBase, 76 DMSR). Over a pairwise censored mean (SD) follow-up of 1.4 (0.7) years, the ARR ratio was higher in patients treated with rituximab than in those treated with ocrelizumab (rate ratio, 1.8; 95% CI, 1.4-2.4; ARR, 0.20 vs 0.09; P < .001). The cumulative hazard of relapses was higher among patients treated with rituximab than those treated with ocrelizumab (hazard ratio, 2.1; 95% CI, 1.5-3.0). No difference in the risk of disability accumulation was observed between groups. Results were confirmed in sensitivity analyses. Conclusion: In this noninferiority comparative effectiveness observational cohort study, results did not show noninferiority of treatment with rituximab compared with ocrelizumab. As administered in everyday practice, rituximab was associated with a higher risk of relapses than ocrelizumab. The efficacy of rituximab and ocrelizumab administered at uniform doses and intervals is being further evaluated in randomized noninferiority clinical trials.

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.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.080
GPT teacher head0.308
Teacher spread0.228 · 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