Feasibility of an International Multiple Sclerosis Rehabilitation Data Repository
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) rehabilitation evidence is limited due to methodological factors, which may be addressed by a data repository. We describe the perceived challenges of, motivators for, interest in participating in, and key features of an international MS rehabilitation data repository. METHODS: A multimethod sequential investigation was performed with the results of two focus groups, using nominal group technique, and study aims informing the development of an online questionnaire. Percentage agreement and key quotations illustrated questionnaire findings. Subgroup comparisons were made between clinicians and researchers and between participants in North America and Europe. RESULTS: Rehabilitation professionals from 25 countries participated (focus groups: n = 21; questionnaire: n = 166). The top ten challenges (C) and motivators (M) identified by the focus groups were database control/management (C); ethical/legal concerns (C); data quality (C); time, effort, and cost (C); best practice (M); uniformity (C); sustainability (C); deeper analysis (M); collaboration (M); and identifying research needs (M). Percentage agreement with questionnaire statements regarding challenges to, motivators for, interest in, and key features of a successful repository was at least 80%, 85%, 72%, and 83%, respectively, across each group of statements. Questionnaire subgroup analysis revealed a few differences (P < .05), including that clinicians more strongly identified with improving best practice as a motivator. CONCLUSIONS: Findings support clinician and researcher interest in and potential for success of an international MS rehabilitation data repository if prioritized challenges and motivators are addressed and key features are included.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.331 | 0.387 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".