Prévalence de l’antigène de surface du virus de l’hépatite B et facteurs associés chez des militaires sénégalais envoyés en mission au Darfour
Bibliographic record
Abstract
INTRODUCTION: In Senegal, 85% of the adult population have been exposed to the hepatitis B virus and about 11% of them are chronic surface antigen (HBsAg) carriers. This infection is poorly documented among Senegalese Armed Forces. The aim of this study was to assess the prevalence of HBsAg in Senegalese military personnel on mission to Darfur (Sudan) and to identify its associated factors. METHODS: We conducted a cross-sectional study among Senegalese military personnel stationed in Darfur from 1 July 2014 to 31 July 2014. HBsAg test was performed on serum of participants using immunochromatographic method. The search for associated factors was carried out using multivariate logistic regression. RESULTS: Our study included 169 male military personnel. The average age was 36.6 ± 9.5 years. A history of familial chronic liver disease, blood exposure and sexual exposure were found in 12.4%, 24.9% and 45.6% of the study population respectively. HBsAg was found in 24 participants [14.2% (CI 95% = 8.9-19.5)]. After adjusting for potential confounding factors, age (OR = 0.9 CI 95% = 0.9-1.0), university level (OR = 9.5 CI 95% = 1.3 - 67 , 1>) and sexual exposure (OR = 3.3 <; CI 95% = 1.0 - 10.3) were independently associated with hepatitis B. CONCLUSION: Our study shows high prevalence of HBsAg and underlines the need for further evaluation of hepatitis B in this population.
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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 | 0.001 |
| 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".