Migraine management during pregnancy, breastfeeding and in women planning pregnancy
Bibliographic record
Abstract
Migraine is a common neurological disorder that predominantly affects women during their reproductive years, presenting unique challenges in the context of pregnancy, breastfeeding, and pregnancy planning. In the present review, we intend to summarize those challenges and propose possible solutions. Women with migraine, particularly those with aura, face an increased risk of pregnancy-related complications, including preeclampsia, stroke, and preterm birth, highlighting the need for careful monitoring throughout gestation. When migraine persists during pregnancy, management should prioritize non-pharmacological approaches, with a strong emphasis on lifestyle modifications and behavioral therapies. In some settings, non-invasive neuromodulation may also be a reasonable option. However, disabling migraine should not be left untreated and may require pharmacological management. Pharmacological treatments should be chosen primarily based on safety considerations, as many migraine medications are not suitable for use during pregnancy. Given the limited safety data available for several treatments, shared decision-making between patients and healthcare providers is essential. During breastfeeding, medication selection should focus on minimizing infant exposure while ensuring effective migraine control for the mother. In women of childbearing potential, caution is needed when prescribing certain migraine treatments, as unplanned pregnancies can occur. Special considerations should also be given to those requiring preventive treatment while planning pregnancy. Given the complexities of migraine management in this population, an individualized approach is crucial to balancing maternal well-being with fetal and infant safety.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".