Reproductive decision making after the diagnosis of multiple sclerosis (MS)
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine reproductive practices and attitudes of North Americans diagnosed with multiple sclerosis (MS) and the reasons for their reproductive decision making. METHODS: A self-administered questionnaire on reproductive practices was mailed to 13,312 registrants of the North American Research Committee on Multiple Sclerosis (NARCOMS) database who met inclusion criteria for the study. Completed questionnaires were then returned to the authors in an anonymous format for analysis. RESULTS: Among 5949 participants, the majority of respondents (79.1%) did not become pregnant following diagnosis of MS. Of these, 34.5% cited MS-related reasons for this decision. The most common MS-related reasons were symptoms interfering with parenting (71.2%), followed by concerns of burdening partner (50.7%) and of children inheriting MS (34.7%). The most common reason unrelated to MS for not having children was that they already have a "completed family" (55.6%). Of the 20.9% of participants who decided to become pregnant (or father a pregnancy) following a diagnosis of MS, 49.5% had two or more pregnancies. CONCLUSION: This study indicates that an MS diagnosis does not completely deter the consideration of childbearing in MS patients of both genders.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".