Canadian epilepsy priority-setting partnership: Toward a new national research agenda
Bibliographic record
Abstract
BACKGROUND: Health research agendas are often set by researchers or by industry and may not reflect the needs and priorities of end users. This priority-setting partnership (PSP) for epilepsy was undertaken to identify the most pressing unanswered questions about epilepsy and seizures from the perspective of people with epilepsy (PWE) and their care providers. METHODS: Using the methodology developed by the James Lind Alliance (JLA), evidence uncertainties were gathered via online surveys from stakeholders across Canada. Submissions were formed into summary questions and checked against existing evidence to determine if they were true uncertainties. Verified uncertainties were then ranked by patients, caregivers, and healthcare providers and a final workshop was held to reach a consensus on the top 10 priorities. RESULTS: The final top 10 list reflects the priority areas of focus for research as identified by the Canadian epilepsy community, including genetic markers for diagnosis and treatment, concerns about living with the long-term effects of epilepsy, and addressing knowledge gaps in etiology and treatment approaches. CONCLUSION: This project represents the first systematic evidence of patient- and clinician-centered research priorities for epilepsy. The results of this priority-setting exercise provide an opportunity for researchers and funding agencies to align their agendas with the values and needs of the epilepsy community in order to improve clinical outcomes and quality of life (QOL) for PWE.
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 | 0.044 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".