A comparison of multiple sclerosis disease activity after discontinuation of fingolimod and placebo
Bibliographic record
Abstract
BACKGROUND: Cases of higher-than-expected disease activity have been reported following fingolimod discontinuation. OBJECTIVE: The objective of this paper is to assess the risk of substantially higher-than-expected disease activity post-study drug discontinuation (SDD) at the individual patient level using data from the Phase III, placebo-controlled FREEDOMS and FREEDOMS II trials. METHODS: Baseline gadolinium-enhancing T1-lesion volumes were used to statistically model the expected level of MRI disease activity post-SDD. Patients exceeding this level were classed as "MRI outliers." Patients with an unusually high increase in Expanded Disability Status Scale score, hospitalization for relapse, severe relapse, or relapse with incomplete recovery post-SDD were classed as "clinical outliers." RESULTS: In FREEDOMS, the number of MRI outliers post-SDD was 2/69 (2.9%), 1/65 (1.5%) and 7/83 (8.4%) for the placebo, fingolimod 0.5 mg, and fingolimod 1.25 mg groups, respectively. In FREEDOMS II, the corresponding numbers were 4/72 (5.6%), 6/79 (7.6%) and 3/73 (4.1%). The number of clinical outliers across both trials was low. No consistent evidence of placebo vs fingolimod, dose-related or inter-trial patterns was discernable. CONCLUSION: The low number of clinical and MRI outliers and lack of any discernible pattern within and between trials, including between placebo and fingolimod, argues against a systematic risk of higher-than-expected recurrence of disease activity following discontinuation of fingolimod.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".