Ocrelizumab extended‐interval dosing in multiple sclerosis during <scp>SARS‐CoV</scp>‐2 pandemic: a real‐world experience
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: During the COVID-19 pandemic, ocrelizumab administration was frequently postponed because of a lack of safety information and to favour vaccination. The clinical implications of ocrelizumab administration delay in multiple sclerosis (MS) patients were assessed. METHODS: Relapsing (RMS) and primary progressive (PPMS) MS patients receiving ocrelizumab for at least 6 months at our centre were retrospectively classified, according to the possible occurrence of a delay (≥4 weeks) in treatment administration. Patients were categorized in the extended-interval dosing (EID) group in the presence of at least one delayed infusion; otherwise they were considered as part of the standard interval dosing (SID) cohort. MS history, magnetic resonance imaging examinations and B-cell counts were also retrospectively collected and analysed. RESULTS: A total of 213 RMS and 61 PPMS patients were enrolled; 115 RMS and 29 PPMS patients had been treated according to the SID regimen, whilst 98 RMS and 32 PPMS patients were included in the EID cohort. Average follow-up after delay was 1.28 ± 0.7 years in the EID cohort. In RMS, comparing SID and EID patients, no differences were found considering the occurrence of clinical relapses (9.6% vs. 16.3%, p = 0.338), magnetic resonance imaging activity (9.8% vs. 14.1%, p = 0.374) or disability progression (11.3% vs. 18.4%, p = 0.103). Similar findings were observed in PPMS patients. In the pooled EID group, treatment delay correlated with CD19-positive relative (r = 0.530, p < 0.001) and absolute (r = 0.491, p < 0.001) cell counts, without implications on disease activity. CONCLUSIONS: Sporadic ocrelizumab administration delay granted sustained treatment efficacy in our cohort. Prospective data should be obtained to confirm these observations and set up systematic extended-interval regimens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".