Sex-Specific Health Challenges: A Study of Women with Epilepsy Across the Lifespan
Bibliographic record
Abstract
In Canada, it is estimated that 300,000 people are living with epilepsy, half of whom are women (Patel & Grindrod, 2020). A diagnosis of epilepsy brings inherent psychosocial challenges including increased risks of developing mood disorders, interpersonal and institutional stigma, reduced employment rates, increased mortality rates, low socioeconomic status, and reduced quality of life (Thomas & Nair, 2011; Josephson & Jetté, 2017; Josephson et al., 2017). In addition to the inherent challenges of living with epilepsy, women with epilepsy (WWE) face difficulties with fertility and family planning, contraception, teratogenicity, sexual function, management of care during and after pregnancy, safety while caring for children, hormonal influences on seizure frequency, and additional bone health issues (Aylward, 2008; Crawford et al., 1999; Herzog et al., 2016; Noe, 2007; Pack et al., 2009). The purpose of this study was to explore the implementation of the recommended care for WWE for sex-specific health issues from the perspective of the WWE. Using a cross-sectional survey, we investigated the experience of WWE regarding menstruation, family planning, prenatal and perinatal care, hormonal influences and therapies, and bone health. From the perspective of knowledge translation, this study is one measure of the recommended care reaching the patient.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".