The road to conception for women with multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: The objective of this prospective "real world" study is to gain insight into the different "roads to conception" that women with MS take as part of the prospective Canadian Multiple Sclerosis Pregnancy Study (CANPREG-MS). METHODS: Participants are women with MS who are planning a pregnancy. Data cut-off for analyses was April 30, 2020. RESULTS: We believe this is the first prospective National study of women with MS planning pregnancies.The data are for the first 44 women enrolled of whom 26 achieved pregnancy by cut-off date. Seven women used assisted reproductive technologies (ARTs); 6 stopped disease modifying therapy (DMT) against their neurologists' recommendations; 6 had an interruption(s) in trying to conceive due to MS relapses, MRI-detected inflammation, or limited "windows of opportunity" between DMT courses. CONCLUSION: The study illustrates the roads that women take to conception, even if they are on the same therapy and have similar clinical expression of MS. Advice given by treating neurologists on washout periods show discrepancies. This paper highlights the real problem that there is no definitive, international consensus on managing these women due to the lack of "real world" data and thus the goal of CANPREG-MS is to provide such real world data.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".