The Managing Epilepsy Well Network:
Bibliographic record
Abstract
Epilepsy, a complex spectrum of disorders, affects about 2.9 million people in the U.S. Similar to other chronic disorders, people with epilepsy face challenges related to management of the disorder, its treatment, co-occurring depression, disability, social disadvantages, and stigma. Two national conferences on public health and epilepsy (1997, 2003) and a 2012 IOM report on the public health dimensions of epilepsy highlighted important knowledge gaps and emphasized the need for evidence-based, scalable epilepsy self-management programs. The Centers for Disease Control and Prevention translated recommendations on self-management research and dissemination into an applied research program through the Prevention Research Centers Managing Epilepsy Well (MEW) Network. MEW Network objectives are to advance epilepsy self-management research by developing effective interventions that can be broadly disseminated for use in people's homes, healthcare providers' offices, or in community settings. The aim of this report is to provide an update on the MEW Network research pipeline, which spans efficacy, effectiveness, and dissemination. Many of the interventions use e-health strategies to eliminate barriers to care (e.g., lack of transportation, functional limitations, and stigma). Strengths of this mature research network are the culture of collaboration, community-based partnerships, e-health methods, and its portfolio of prevention activities, which range from efficacy studies engaging hard-to-reach groups, to initiatives focused on provider training and knowledge translation. The MEW Network works with organizations across the country to expand its capacity, help leverage funding and other resources, and enhance the development, dissemination, and sustainability of MEW Network programs and tools. Guided by national initiatives targeting chronic disease or epilepsy burden since 2007, the MEW Network has been responsible for more than 43 scientific journal articles, two study reports, seven book chapters, and 62 presentations and posters. To date, two programs have been adopted and disseminated by the national Epilepsy Foundation, state Epilepsy Foundation affiliates, and other stakeholders. Recent expansion of the MEW Network membership will help to extend future reach and public health impact.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.100 | 0.032 |
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".