Prevalence and associated factors of Treponema pallidum infection in a rural area of southwestern China
Bibliographic record
Abstract
BACKGROUND: Epidemiological data on Treponema pallidum infection are scarce from the southwestern region of China. The purpose of this study was to determine the distribution and determinants of T. pallidum infection in the region. METHODS: A community-based cross-sectional study of 2608 participants aged ≥14 years was conducted in a rural area of southwestern China in 2014-15. A pretested questionnaire was used to collect sociodemographic characteristics and other factors associated with T. pallidum infection. The diagnoses of T. pallidum, human immunodeficiency virus (HIV), hepatitis B virus (HBV) and hepatitis C virus (HCV) infections were determined by commercial test kits. Logistic regression analysis was used to determine the correlates for T. pallidum infection, and adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were calculated. RESULTS: The prevalence of T. pallidum infection was 1.2% (95% CI 0.8 to 1.7%). Risk factors varied by gender. In the male group, T. pallidum infection was significantly associated with ever injection drug use (aOR = 9.42, 95% CI 2.47 to 35.87) and HCV infection (aOR = 13.28, 95% CI 3.20 to 51.70). In the female group, correlates for T. pallidum infection included spouse having syphilis (aOR = 126.66, 95% CI 7.58 to 2122.94), ever having blood transfusion (aOR = 10.51, 95% CI 1.58 to 41.21) and HBV infection (aOR = 4.19, 95% CI 1.35 to 10.93). CONCLUSIONS: The prevalence of T. pallidum infection was high in the rural area of southwestern China. Correlates for T. pallidum infection varied with sex specific. Intervention should be developed for the prevention and control of T. pallidum infection.
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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 | 0.001 | 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 | 0.000 | 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".