SARS-CoV-2 omicron breakthrough infections in patients with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: It is unclear which patients with multiple sclerosis (MS) are most susceptible for omicron breakthrough infections. METHODS: We assessed omicron breakthrough infections in vaccinated patients with MS with and without disease-modifying therapies enrolled in an ongoing large prospective study. We longitudinally studied humoral responses after primary and booster vaccinations and breakthrough infections. RESULTS: Omicron breakthrough infections were reported in 110/312 (36%) patients with MS, and in 105/110 (96%) infections were mild. Omicron breakthrough infections occurred more frequently in patients treated with anti-CD20 therapies and sphingosine-1 phosphate receptor (S1PR) modulators, patients with impaired humoral responses after primary immunisation (regardless of treatment) and patients without prior SARS-CoV-2 infections. After infection, antibody titres increased in patients on S1PR modulator treatment while anti-CD20 treated patients did not show an increase. CONCLUSIONS: SARS-COV-2 omicron breakthrough infections are more prevalent in patients with MS on anti-CD20 therapies and S1PR modulators compared with other patients with MS, which correlated with decreased humoral responses after vaccination. Humoral responses after infection were higher in S1PR modulator-treated patients in comparison to patients on anti-CD20 therapies, suggesting that immunological protection from contracting infection or repeated exposures may differ between these therapies.
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 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".