Sub-analysis of geographical variations in the 2-year observational COPTIMIZE trial of patients with relapsing–remitting multiple sclerosis converting to glatiramer acetate
Bibliographic record
Abstract
BACKGROUND: Studies suggest that patients with relapsing-remitting multiple sclerosis (RRMS) who fail to benefit from a disease-modifying treatment (DMT) may benefit from converting to another DMT class. COPTIMIZE was a 24-month observational study designed to assess the disease course of patients converting to glatiramer acetate (GA) 20 mg daily from another DMT and the association of disease characteristics and reasons for converting. This sub-analysis was to determine if any findings varied by three geographic locations: Latin America (LA), Canada and Western Europe (CWE), and Eastern Europe (EE). METHODS: A total of 668 patients were included (263 LA, 248 CWE, 157 EE) in an analysis of annualized relapse rate (ARR) and annualized rate of deterioration (ARD), as well as secondary endpoints including reason for DMT switch and changes in disability and fatigue scores. Repeated-measures analysis of variance and log transformation were used to analyze ARR and ARD, whereas the Wilcoxon signed rank test was used for secondary endpoints. RESULTS: The sub-analysis of treatment outcomes stratified by region showed that Latin American patients had higher ARR before conversion to GA compared with patients from the other two areas and subsequently experienced the largest reduction in ARR. Latin American patients also had higher baseline rates of comorbidities and relapses with incomplete remissions and improved more than those in the other two regions based on measures of fatigue, quality of life, depression, and cognition scores. Latin American patients also generally had a better perception of the benefits associated with their conversion to GA in terms of efficacy and adverse events. CONCLUSIONS: These findings indicate that, in RRMS patients, converting to GA is associated with positive treatment outcomes regardless of geographic location. However, the reasons for converting and the type and degree of any associated benefits appear to vary depending on various factors, including patients' geographical location.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".