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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".