Decreasing the Epilepsy Treatment Gap in Tena, Ecuador: A Report From 2021 to 2023
Bibliographic record
Abstract
Background Neurological care in rural areas such as Tena, Ecuador, remains critically low due to geographic, economic, and systemic barriers. Tena, located in the Amazon region, has limited access to specialized neurological services, creating significant health disparities. Since 2009, the International Neurology Foundation (INF) has partnered with the Hospital José María Velasco Ibarra to address these challenges. Methodology This retrospective analysis summarizes data from the INF medical service relief trip (MSRT) conducted in Tena from 2021 to 2023. Clinical records, interviews with providers, and MSRT reports were reviewed to assess patient demographics, diagnoses, treatments, and interventions. Descriptive statistics and thematic analysis were used to identify trends and insights. Results Over three years, 751 patients were treated, with epilepsy being the most common diagnosis (265 cases). Children under the age of 10 years represented the largest patient group. Key achievements included the donation of electroencephalography equipment, enabling local epilepsy diagnostics, and training local healthcare providers. Persistent challenges included limited imaging resources, inconsistent medication supply, and barriers related to language and transportation. Conclusions INF's initiatives have significantly improved access to neurological care in Tena, enhancing diagnostic capabilities and providing critical training. Sustainable progress requires investment in infrastructure, expanded training programs, and consistent medication availability. The Tena experience serves as a model for reducing health disparities and improving neurological care in resource-limited settings, aligning with global health equity priorities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".