Decentralized, primary-care delivered epilepsy services in Burera District, Rwanda: Service use, feasibility, and treatment
Bibliographic record
Abstract
BACKGROUND: Integrating epilepsy care into primary care settings could reduce the global burden of illness attributable to epilepsy. Since 2012, the Rwandan Ministry of Health and the international nonprofit Partners In Health have collaboratively used a multi-faceted implementation program- MESH MH-to integrate and scale-up care for epilepsy and mental disorders within rural primary care settings in Burera district, Rwanda. We here describe demographics, service use and treatment patterns for patients with epilepsy seeking care at MESH-MH supported primary care health centers. METHODS AND FINDINGS: This was a retrospective cohort study using routinely collected data from fifteen health centers in Burera district, from January 2015 to December 2016. 286 patients with epilepsy completed 3307 visits at MESH-MH participating health centers over a two year period (Jan 1st 2015 to Dec 31st 2016). Men were over twice as likely to be diagnosed with epilepsy than women (OR 2.38, CI [1.77-3.19]), and children under 10 were thirteen times as likely to be diagnosed with epilepsy as those 10 and older (OR 13.27, CI [7.18-24.51]). Carbamazepine monotherapy was prescribed most frequently (34% of patients). CONCLUSION: Task-sharing of epilepsy care to primary care via implementation programs such as MESH-MH has the potential to reduce the global burden of illness attributable to epilepsy.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".