The EnvIMS Study: Design and Methodology of an International Case-Control Study of Environmental Risk Factors in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is a chronic disease of the central nervous system, often resulting in significant neurological disability. The causes of MS are not known; however, the incidence of MS is increasing, thereby suggesting that changes in lifestyle and/or environmental factors may be responsible. On this background, the Environmental Risk Factors in MS Study or EnvIMS study was designed to further explore the etiology of MS. The design and methodology are described, providing details to enable investigators to (i) use our experiences to design their own studies; (ii) take advantage of, and build on the methodological work completed for, the EnvIMS study; (iii) become aware of this data source that is available for use by the research community. METHODS: EnvIMS is a multinational case-control study, enrolling 2,800 cases with MS and 5,012 population-based controls in Canada, Italy, Norway, Serbia and Sweden. The study was designed to investigate the most commonly implicated risk factors for MS etiology using a self-report questionnaire. RESULTS/CONCLUSIONS: The use of a common methodology to study MS etiology across several countries enhances the comparability of results in different geographic regions and research settings, reduces the resources required for study design and enhances the opportunity for data harmonization.
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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 | 0.039 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".