Intermediate outcomes for clinical trials of multiple sclerosis rehabilitation interventions: Conceptual and practical considerations
Bibliographic record
Abstract
Background:Rehabilitation is an essential health care service and a critical component of comprehensive multiple sclerosis (MS) care.Objective:As part of a 2-day meeting hosted by the International Advisory Committee on Clinical Trials in MS in December 2022, a panel initiated a discussion on the conceptual and practical issues related to selecting intermediate outcomes for clinical trials of MS rehabilitation interventions.Results:The overarching goal of rehabilitation – optimal functioning – was acknowledged as a complex biopsychosocial phenomenon that varies with patient priorities and environmental context. This complexity means that multiple causal pathways and potential intermediate outcomes must be carefully considered during the design of clinical trials in MS rehabilitation that aim to improve functioning. In addition, practical issues must be considered such as psychometric properties of outcome measures, measure type, and characteristics of the target population, including severity of dysfunction.Conclusion:This article uses the International Classification of Functioning, Disability and Health as a foundation for determining relevant intermediate outcomes for clinical trials of MS rehabilitation interventions.
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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 | 0.733 | 0.791 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.011 | 0.018 |
| 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".