Disease-modifying therapy initiation patterns in multiple sclerosis in three large MS populations
Bibliographic record
Abstract
Background: Treatment guidelines recommend early disease-modifying therapy (DMT) initiation after diagnosis of multiple sclerosis (MS). Multinational comparative studies that assess time to DMT initiation in MS may allow detection of barriers inherent to healthcare systems to explain potential adverse systematic delays in commencing DMTs. Objectives: To investigate and compare the time to first DMT and its association with sociodemographic and clinical variables after MS diagnosis in three large MS registries. Design: This observational study was conducted using data from the German MS Registry (GMSR), the North American Research Committee on MS Registry (NARCOMS, US data only), and the United Kingdom MS Registry (UKMSR, both self- and clinician-reported). Methods: Data from relapsing people with MS (PwMS), with a diagnosis of MS between 2014 and 2019, and available DMT and disability status were pooled using a meta-analytic approach. Results: A total of 5395 PwMS were included in the analysis (GMSR: n = 2658; NARCOMS: n = 447; UKMSR: n = 2290). Kaplan–Meier estimates for the time to first DMT [median months (95% CI)] were 2.0 (1.9–2.0), 3.0 (2–4), and 9.0 (7.7–10.6) for GMSR, NARCOMS, and UKMSR, respectively. Pooled multivariable Cox regression demonstrated shorter time to first DMT for PwMS diagnosed after 2017 [1.65 (1.42–1.92), p < 0.01], and longer time to DMT when a higher-efficacy DMT was selected (0.69 (0.54–0.90), p < 0.0001]. Conclusion: Time to DMT initiation differs across the populations studied, indicating that barriers may exist in early access to DMT, particularly in the United Kingdom. However, a consistent decrease in time to DMT initiation was noted since 2017 across all registries. Further studies are warranted comparing the effects of time to DMT and time to higher-efficacy DMT on long-term outcome.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".