Cognitive Status in People With Epilepsy in the Republic of Guinea: A Prospective, Case–Control Study
Bibliographic record
Abstract
OBJECTIVE: People with epilepsy (PWE) may experience cognitive deficits but fail to undergo formal evaluation. This study compares cognitive status between PWE and healthy controls in the West African Republic of Guinea. METHODS: A cross-sectional, case-control study was conducted in sequential recruitment phases (July 2024-July 2025) at Ignace Deen Hospital, Conakry. Adult (≥ 18 years) PWE enrolled consecutively, excluding those with a seizure within the past 24 h. Controls were healthy adults accompanying PWE at the hospital. Cognitive status was assessed with the Montreal Cognitive Assessment (MoCA) in French or translated into the patient's preferred language (Pular, Susu, Maninka, Kissi) as needed. RESULTS: We enrolled 100 PWE (mean age 30.4 years, range 18-71, SD = 12.0) and 100 controls (mean age 39.4 years, range 19-70, SD = 12.3). Although 93% of PWE had previously used anti-seizure medications (ASMs), only 85% were currently receiving treatment and 50% reported interrupted access to ASMs, primarily due to cost barriers. The mean MoCA score of controls (21.8, SD = 4.9) was higher than that of PWE (17.9, SD = 6.1; mean difference -4.2, 95% CI [-5.6, -2.8], SE = 0.69, p < 0.001), adjusted for education level, sex, age, and language. Participants who attended lower secondary, upper secondary, or university education scored 4.9, 5.3, and 8.3 points higher, respectively, than those with no school or primary education (all p < 0.001). Speaking an indigenous language was on average associated with a 2.5-point decline in MoCA scores (95% CI [-3.8, -1.2], SE = 0.65, p < 0.001). INTERPRETATION: PWE in Guinea demonstrated significantly lower cognitive performance on the MoCA compared to healthy controls, even after adjusting for covariates.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".