Temporal profile of lymphocyte counts and relationship with infections with fingolimod therapy in paediatric patients with multiple sclerosis: Results from the PARADIG <i>MS</i> study
Bibliographic record
Abstract
Background: Reduction in absolute lymphocyte count (ALC) is expected with fingolimod treatment. Objective: To evaluate the effect of fingolimod 0.5 mg versus intramuscular interferon β-1a (30 μg) on ALC and its relationship with infections in paediatric-onset multiple sclerosis (POMS) up to 4 years. Methods: We assessed ALC at baseline, monthly till 3 months, and every 3 months (core phase) and with variable periodicity (extension phase) of Phase 3 PARADIG MS study ( N = 215). Incidence rates (IRs) of infection-related adverse events ( inf AEs)/100 patient-years were analysed by on-study nadir ALC. Results: With fingolimod, ALC rapidly reduced to 29.9%–34.4% of baseline values within 2 weeks and remained stable thereafter; no relevant changes observed with interferon. IRs of inf AEs were 67.6 with fingolimod and 61.8 with interferon; IR ratios with respect to interferon, overall: 1.09, by nadir ALC 0.2–0.4 × 10 9 /L: 1.13 and >0.4 × 10 9 /L: 0.91. Three patients had a single episode of ALC <0.2 × 10 9 /L (core phase). No opportunistic infections were observed and infection risk did not increase during the extension phase. Conclusion: In paediatric patients, the overall incidence of infections was comparable between fingolimod and interferon. No association was observed between nadir ALC and infections in POMS, although sample size may have been too small to rule an association.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".