Epilepsy for primary health care: a cost‐effective Latin American E‐learning initiative
Bibliographic record
Abstract
A lack of neurologists in Latin America forces primary health care providers to manage epilepsy. With the main goal of improving diagnostic and therapeutic management of patients with epilepsy through training of physicians in the primary health care level, the International League Against Epilepsy Education Commission (2013-2017) created a low-cost, regional, virtual course. The course, set-up in Moodle platform, was structured in eight modules, each lasting for a week. Teaching was based on written didactic material, videos, and interactive discussions, both in Spanish and Portuguese. Topics included epidemiology, diagnosis, classification, treatment, prognosis, social issues, and epilepsy policies. Each course was limited to 50 participants and priority was given to general practitioners. Certification was given to those approving the final examination. Since 2015, five courses have been developed, involving 143 participants from 17 countries and 21 tutors. Of the participants, 61% worked in primary health care services. A total of 129 participants (90%) completed the course, and 110 submitted the final examination with an approval rate of 95%. From 85 participants completing the course evaluation, 98% would recommend the course to other colleagues, and 99% showed interest in taking other similar courses. High self-confidence for the management of patients with epilepsy increased from 21% at baseline to 73% after the course. The online course on epilepsy for primary care physicians in Latin America was shown to be a cost-effective course, with good retention and excellent approval rates. Our current challenges include periodic updating, complete self-sustainability, and exploring different strategies to reach our target audience more effectively.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".