Anti-Hev Antibody Prevalence in Three Distinct Regions of Turkey and its Relationship with Age, Gender, Education and Abortions
Bibliographic record
Abstract
Hepatitis E virus (HEV) causes epidemics in developing countries such as India, Burma, Indonesia, Chad and China, but it causes sporadic cases in developed countries such as the U.S.A., Canada and the U.K. Turkey represents a bridge between HEV endemic and non-endemic areas, and HEV may cause epidemics in Turkey. In this study, the prevalence of HEV in three distinct regions of Turkey and its relationship with age, gender, education and abortions was investigated. Nine hundred ten randomly selected cases from three cities in three geographic regions of Turkey (Manisa from the Aegean region, Elmadağ/Ankara from Central Anatolia and Diyarbakır from Southeastern Anatolia) were enrolled in the study. After informed consent was obtained, the subjects completed a detailed questionnaire including questions about age, sex, education, and the number of pregnancies, abortions, stillbirths and live babies, and if there was a history of abortion from icteric pregnancy. We researched anti-HEV antibodies in the serum samples of subjects using ELISA. The overall anti-HEV antibody seroprevalence rate was 6.3% (57/910). It was 2.7% in Elmadağ/Ankara, 3.8% in Manisa and 11.7% in Diyarbakır. There was a significant difference between Diyarbakır and the other two regions (p < 0.0001). No significant difference was observed between the other parameters. In conclusion, the overall anti-HEV antibody seroprevalence rate was 6.3% and these rates increased with age.
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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".