MSBase: an international, online registry and platform for collaborative outcomes research in multiple sclerosis
Bibliographic record
Abstract
Observational cohort studies are a powerful tool to assess the long-term outcome in chronic diseases. This study design has been utilized in local and regional outcome studies in multiple sclerosis (MS) and has yielded invaluable epidemiological information. The World Wide Web now provides an excellent opportunity for an international, collaborative cohort study of MS outcomes. A web platform--MSBase--has been designed to collect prospective data on patients with MS. It is purely observational, enabling participating neurologists to contribute data on diagnosis, treatment and progress, to review anonymous aggregate data and to benchmark their patient population against other patient subsets or the entire dataset. MSBase facilitates collaborative research by allowing the online creation of investigator-initiated regional, national and international substudies. The registry aims to answer epidemiological questions that can only be addressed by prospective assessments of large patient cohorts. The registry is funded through the independent MSBase Foundation, and governed by an International Scientific Advisory Board. The MSBase Foundation commenced operations in July 2004 and since then, 22 neurologists from 11 countries have joined MSBase and are contributing 2400 patients to the total data pool.
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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 | 0.029 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.013 |
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".