Expert opinion on the use of cladribine tablets in clinical practice
Bibliographic record
Abstract
BACKGROUND: Gaps in current product labels and a lack of detailed clinical guidelines leaves clinicians' questions on the practical management of patients receiving cladribine tablets for the treatment of relapsing multiple sclerosis (MS) unanswered. We describe a consensus-based programme led by international MS experts with the aim of providing recommendations to support the use of cladribine tablets in clinical practice. METHODS: A steering committee (SC) of nine international MS experts led the programme and developed 11 clinical questions concerning the practical use of cladribine tablets. Statements to address each question were drafted using available evidence, expert experiences and perspectives from the SC and an extended faculty of 33 MS experts, representing 19 countries. Consensus on recommendations was achieved when ⩾75% of respondents expressed an agreement score of 7-9, on a 9-point scale. RESULTS: Consensus was achieved on 46 out of 47 recommendations. Expert-agreed practical recommendations are provided on topics including: the definition of highly active disease; patterns of treatment response and suboptimal response with cladribine tablets; management of pregnancy planning and malignancy risk, infection risk and immune function, and switching to and from cladribine tablets. CONCLUSION: These expert recommendations provide up-to-date relevant guidance on the use of cladribine tablets in clinical practice.
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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 | 0.024 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".