Brain health: time matters in multiple sclerosis
Bibliographic record
Abstract
INTRODUCTION: We present international consensus recommendations for improving diagnosis, management and treatment access in multiple sclerosis (MS). Our vision is that these will be used widely among those committed to creating a better future for people with MS and their families. METHODS: Structured discussions and literature searches conducted in 2015 examined the personal and economic impact of MS, current practice in diagnosis, treatment and management, definitions of disease activity and barriers to accessing disease-modifying therapies (DMTs). RESULTS: Delays often occur before a person with symptoms suggestive of MS sees a neurologist. Campaigns to raise awareness of MS are needed, as are initiatives to improve access to MS healthcare professionals and services. We recommend a clear treatment goal: to maximize neurological reserve, cognitive function and physical function by reducing disease activity. Treatment should start early, with DMT and lifestyle measures. All parameters that predict relapses and disability progression should be included in the definition of disease activity and monitored regularly when practical. On suboptimal control of disease activity, switching to a DMT with a different mechanism of action should be considered. A shared decision-making process that embodies dialogue and considers all appropriate DMTs should be implemented. Monitoring data should be recorded formally in registries to generate real-world evidence. In many jurisdictions, access to DMTs is limited. To improve treatment access the relevant bodies should consider all costs to all parties when conducting economic evaluations and encourage the continuing investigation, development and use of cost-effective therapeutic strategies and alternative financing models. CONCLUSIONS: The consensus findings of an international author group recommend a therapeutic strategy based on proactive monitoring and shared decision-making in MS. Early diagnosis and improved treatment access are also key components.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".