Oral fingolimod (FTY720) in relapsing multiple sclerosis: impact on health-related quality of life in a phase II study
Bibliographic record
Abstract
BACKGROUND: Health-related quality of life (HRQoL) worsens with multiple sclerosis (MS) relapses and disease progression. Common symptoms including depression and fatigue may contribute to poor HRQoL. OBJECTIVES: To report exploratory analyses assessing the impact of fingolimod (FTY720) on HRQoL and depression in a phase II study of relapsing MS. METHODS: The Hamburg Quality of Life Questionnaire in MS (HAQUAMS) and Beck Depression Inventory second edition (BDI-II) scores were assessed during a 6-month, placebo-controlled study and optional extension. RESULTS: HAQUAMS total score improved with fingolimod and worsened with placebo. Mean score change from baseline to month 6 was -0.02 with fingolimod 1.25 mg (p < 0.05 versus placebo), -0.01 with fingolimod 5.0 mg and + 0.12 with placebo. Categorical data supported a clinically important effect of fingolimod on HRQoL. Fingolimod 1.25 mg was also beneficial over placebo in the fatigue/thinking HAQUAMS sub-domain (p < 0.05 versus placebo). Change in mean BDI-II scores from baseline to month 6 and the proportion of patients with BDI-II scores indicative of clinical depression favored fingolimod 1.25 mg over placebo (p < 0.05 for both). At month 4, mean BDI-II and HAQUAMS total scores appeared to be maintained in fingolimod-treated patients. CONCLUSION: Fingolimod 1.25 mg may improve HRQoL and depression at 6 months compared with placebo in patients with relapsing MS.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".