Embracing the complexity of rehabilitation in multiple sclerosis empowered by the resolution of the World Health Organization
Bibliographic record
Abstract
The World Health Organization (WHO) adopted a historic resolution to strengthen rehabilitation in the health systems. It calls on governments and rehabilitation stakeholders to scale up rehabilitation. According to the global burden of disease database, it is estimated that one out of three persons worldwide can benefit from rehabilitation. Implementing the resolution is particularly challenging, given the complexity of neurorehabilitation for people with MS. A historical perspective of the development of multi-disciplinary rehabilitation, including the foundation of professional rehabilitation organisations, highlights the significant progress made over the past 50 years. Research has also contributed by providing evidence and guidelines to support more predictable and better outcomes. In reality, rehabilitation is complex because MS changes over time and affects many aspects of life. It requires coordination among health care professionals and alignment with diverse and evolving personal goals. In this future perspective, we illustrate societal developments related to health and wellbeing and the challenges of delivering evidence-based, multi-faceted interdisciplinary rehabilitation for people 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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".