Treatment-emergent adverse events occurring early in the treatment course of cladribine tablets in two phase 3 trials in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Treatment-emergent adverse events (TEAEs) that occur close to treatment initiation may negatively affect overall tolerability and adherence. It is important to develop a clear understanding of potential early TEAEs after initiating treatment with cladribine tablets. OBJECTIVE: To identify TEAEs that begin early in the course of treatment in patients enrolled in CLARITY and ORACLE-MS studies. METHODS: analysis of CLARITY and ORACLE-MS safety populations assessed the incidence of TEAEs, serious TEAEs, drug-related TEAEs, and TEAEs leading to discontinuation in patients receiving cladribine tablets or placebo within 2, 6, and 12 weeks after treatment initiation. RESULTS: By Week 12, 61.3% of patients treated with cladribine tablets 3.5 mg/kg and 55.2% treated with placebo experienced a TEAE. More patients receiving cladribine tablets versus placebo experienced a drug-related TEAE by Week 12 (34.7% vs. 23.2%). The most common TEAEs reported with cladribine tablets were: headache (7.2%), lymphopenia (6.8%), and nausea (6.0%). Patients receiving cladribine tablets and placebo reported similar proportions of serious TEAEs (2.2% vs. 1.7%) and TEAEs leading to treatment discontinuation (1.6% vs. 1.4%). CONCLUSION: Cladribine tablets were well tolerated during the first 12 weeks as evidenced by a low incidence of TEAEs leading to treatment discontinuation.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".