Epilepsy in Onchocerciasis Endemic Areas: Systematic Review and Meta-analysis of Population-Based Surveys
Bibliographic record
Abstract
OBJECTIVE: We sought to evaluate the relationship between onchocerciasis prevalence and that of epilepsy using available data collected at community level. DESIGN: We conducted a systematic review and meta-regression of available data. DATA SOURCES: Electronic and paper records on subject area ever produced up to February 2008. REVIEW METHODS: We searched for population-based studies reporting on the prevalence of epilepsy in communities for which onchocerciasis prevalence was available or could be estimated. Two authors independently assessed eligibility and study quality and extracted data. The estimation of point prevalence of onchocerciasis was standardized across studies using appropriate correction factors. Variation in epilepsy prevalence was then analyzed as a function of onchocerciasis endemicity using random-effect logistic models. RESULTS: Eight studies from west (Benin and Nigeria), central (Cameroon and Central African Republic) and east Africa (Uganda, Tanzania and Burundi) met the criteria for inclusion and analysis. Ninety-one communities with a total population of 79,270 individuals screened for epilepsy were included in the analysis. The prevalence of epilepsy ranged from 0 to 8.7% whereas that of onchocerciasis ranged from 5.2 to 100%. Variation in epilepsy prevalence was consistent with a logistic function of onchocerciasis prevalence, with epilepsy prevalence being increased, on average, by 0.4% for each 10% increase in onchocerciasis prevalence. CONCLUSION: These results give further evidence that onchocerciasis is associated with epilepsy and that the disease burden of onchocerciasis might have to be re-estimated by taking into account this relationship.
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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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".