Family planning and contraception in people with multiple sclerosis: perspectives for obstetricians, gynaecologists, and other health care professionals involved in reproductive planning
Bibliographic record
Abstract
PURPOSE: Multiple sclerosis (MS) is often diagnosed in people of reproductive age. However, family planning counselling is not always integrated within MS care. Decisions on family planning can be further complicated by potential side effects associated with several disease-modifying therapies. While neurologists may lack training in contraceptive use and family planning counselling, obstetricians and gynaecologists (OB-GYNs) and other health care professionals involved in reproductive life planning (RHCPs) may lack detailed knowledge and experience around the use of contemporary MS treatments. MATERIAL AND METHODS: Through a modified Delphi consensus programme, a multidisciplinary steering committee of 13 international experts developed practical clinical recommendations on contraceptive use and family planning for people with MS (PwMS). This article offers insights to help OB-GYNs and RHCPs implement these recommendations, focusing on contraceptive decision-making and MS medications. RESULTS: The perspectives discussed emphasise providing education on MS to OB-GYNs and other RHCPs, enabling informed counselling for PwMS and their partners regarding contraception and family planning. Close collaboration among the multidisciplinary team, including neurologists, is crucial in providing reproductive care for PwMS. CONCLUSIONS: The detailed perspectives provided aim to enable OB-GYNs and other RHCPs to provide informed counselling for PwMS and their partners regarding contraception and family planning.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".