Editor’s Pick: Precision Medicine in Neurology: Advancing Care for Female Patients
Bibliographic record
Abstract
Growing evidence suggests that biological sex influences disease risk, clinical presentation, treatment response, and prognosis across neurological conditions. Despite this increasing awareness of important sex differences, neurological research and clinical care remain insufficiently tailored to females. In this review, the authors highlight the importance of integrating sex-specific considerations into precision medicine for neurological disorders. Focusing on five high-prevalence and high-burden neurological conditions (epilepsy, migraine, stroke, multiple sclerosis, and neurodegenerative diseases), this review identifies critical knowledge gaps and actionable opportunities for advancing care for females. Such gaps and opportunities include: 1) improved pregnancy and lactation data in epilepsy; 2) hormonal influences across the menstrual cycle, pregnancy, and menopause in migraine; 3) sex-based disparities in symptom recognition, treatment access, and rehabilitation for stroke patients; 4) the influence of sex hormones on disease onset, progression, and prognosis in multiple sclerosis; and 5) sex differences in pathophysiology and clinical trajectories in neurodegenerative diseases. This review proposes a roadmap for integrating sex-based considerations into three key domains: clinical care, research, and neurology training. Prioritising and advancing these initiatives is essential for improving neurological care and represents a critical step towards equitable precision medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".