P.009 Canadian Survey of the neurological care provided to women living with epilepsy: preliminary results
Bibliographic record
Abstract
Background: In Canada, approximately 300,000 women with epilepsy (WWE) are of childbearing potential. Given the unique aspects of providing care for WWE, our objective was to gather demographic and practice characteristics of health care professionals providing care for WWE to identify potential gaps. Methods: We developed a questionnaire to understand the demographic and practice characteristics of professionals providing care for WWE. We invited all French and English practitioners (physicians, physician assistants and nurse practitioners), recruited through the Canadian League Against Epilepsy (CLAE), Canadian Neurological Sciences Federation (CNSF) and Canadian Epilepsy nursing Group (CENG), to complete our online questionnaire. Results: Preliminary data show 43% were between 32-40 years of age and 76% were medical doctors. Sixty-three percent had been in practice for less than 20 years; 81% considered themselves epilepsy specialists and 66% devoted their practice entirely to epilepsy patients and 78% practiced exclusively in academic centers. Conclusions: Our data shows providers involved in the care of women with epilepsy in Canada are predominantly academic experts in epilepsy. Potential gaps in care include mid-late career physicians, non-specialized health care practitioners, and community-based practices, as less likely to provide care for WWE. This snapshot may provide future insights to the unmet needs of WWE Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".