Multiple sclerosis relapses are associated with increased fatigue and reduced health-related quality of life – A post hoc analysis of the TEMSO and TOWER studies
Bibliographic record
Abstract
BACKGROUND: Two pivotal phase 3 teriflunomide studies provided data on relapses, fatigue, and health-related quality of life (HRQoL) in patients with relapsing forms of multiple sclerosis (MS). OBJECTIVES: Using pooled data from the TEMSO (NCT00134563) and TOWER (NCT00751881) studies, we investigated the association between relapse severity, and changes from baseline to Week 108 in fatigue and HRQoL outcomes. METHODS: Four definitions of relapse severity were applied in this analysis: sequelae post-relapse; relapse leading to hospitalization; relapse requiring intravenous corticosteroids; and intense relapse. We assessed the association between relapse severity and changes in Fatigue Impact Scale score (n=959), physical and mental health component summary scores from the Short Form (SF)-36 questionnaire (n=904), and SF-6D utility index scores (n=820). RESULTS: Irrespective of the definition of relapse severity applied, in patients experiencing severe relapse(s), fatigue was increased and HRQoL was decreased; these changes were statistically significant (p<0.0001), and were also clinically significant in many cases. The greatest worsening in fatigue and HRQoL was observed in patients with relapses leading to hospitalization. CONCLUSIONS: Given that severe relapses adversely affect patient-reported fatigue and HRQoL, prevention of severe relapses should be an important therapeutic aim in the treatment of patients with 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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".