Canadian Multiple Sclerosis Pregnancy Study (CANPREG-MS): Rationale and Methodology
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is the most common cause of neurological disability, other than trauma, among young adults of reproductive age. In contrast to the past, today there is very little lag time from clinical onset to diagnosis. Disease-modifying therapies are also now available outside of clinical trials. However, there is very little evidence-based population data to help an individual with MS make informed decisions with respect to reproductive options. OBJECTIVE: The objective of this study is to develop a Canada-wide, prospective population-based registry of women with MS who are either trying to become pregnant and/or have become pregnant. METHODS: The study represents a "real-world" scenario. Women with MS are invited to participate, regardless of clinical course, therapy, disease duration, and/or disability. The methodology to develop such a registry is very complex making it imperative to understand the design and rationale when interpreting results for clinical purposes. RESULTS: This paper is a comprehensive discussion of the study rationale and methodology. CONCLUSIONS: The study is ongoing, with over 100 potential participants. Numerous future publications are envisioned as the study progresses. The present paper is thus designed to be the key referral paper for subsequent publications in which it will not be possible to provide the necessary detailed information on rationale and methodology.
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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 | 0.082 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".