The prevalence of Strongyloides stercoralis infection in rural population of Bali: A preliminary study
Bibliographic record
Abstract
A cross-sectional study was carried out from March until September 1992 in 4 different geo-climatic rural villages in Bali to assess the prevalence of Strongyloides stercoralis infection related to age, gender and geo-climatic condition. The number of selected samples using multistage stratified random sampling based on age and gender was 2880. Strongyloides larvae in stool samples were identified by modified Harada Mori fecal culture technique. The results showed that the overall prevalence of Strongyloides infection was 1.6%; the prevalence in male was 1.2% and in female was 2.1%, however the gender difference was not statistically significant. In term of age, there was no statistically significant difference. The highest prevalence (2.4%) was found among 7-12 years children, followed by aged 7 years or less (1.6%), 18 years or over (1.6%), and 13-18 years (0.6%). The prevalence of Strongyloides infection related to geo-climatic conditions was significantly difference. The highest prevalence of Strongyloides infection was at wet highland (3.3%), followed by wet lowland (1.5%), dry highland (1.0%), and dry lowland (0.9%). In conclusion, the prevalence of Strongyloides stercoralis infection in Bali was very low. Prevalence of Strongyloides infection was highly related to geo-climatic type of area, but not to gender and age groups. (Med J Indones 2001; 10: 174-7)
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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) | 0.001 | 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".