Risk Factors for Epilepsy in a Rural Area in Tanzania
Bibliographic record
Abstract
BACKGROUND AND METHODS: The high prevalence of epilepsy detected in rural Tanzania by Dr. Jilek-Aall since 1960, was verified by the World Health Organization (WHO) survey on neurological and seizure disorders. Neurologists and psychiatrists further interviewed both patients and controls using standard methods. The presence of possible risk factors was complemented by corroborative evidence through interviewing close relatives and scrutinizing medical records. Seizures were classified based on clinical symptoms and the use of EEG. RESULTS: A family history of epilepsy in first-degree relatives was found in 46.6% of patients, but in only 19.6% of controls. The odds ratio for family history with epilepsy was 3.52 (95% confidence interval, CI 2.4-5.74, p < 0.001). A past history of febrile convulsion was found in 44% of patients in comparison to 23% of the control group which was significant (odds ratio 2.4, 95% CI 1.5-3.8; p < 0.001). A history of intrapartum complications was found in 12.1% of patients and 1.8% of controls (odds ratio 7.3, 95% CI 2.5-25.2; p < 0.002). Head injury was not a significant risk factor for epilepsy in this rural community. CONCLUSION: The results indicated a strongly independent association between four factors and the risk of developing epilepsy. It would seem more likely that previous brain insults/diseases play a significant major role in the cause of epilepsy in the Mahenge area. However, a genetic predisposition to low threshold for convulsions cannot be excluded.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".