PREVALENCE OF HEPATITIS B VIRUS SURFACE ANTIGEN AMONG HOSPITAL PATIENTS IN JASHORE CITY, BANGLADESH, 2017-2018
Bibliographic record
Abstract
Abstract Background Despite having a huge public health burden, Bangladesh lacks recent national surveillance studies on HBV prevalence and epidemiology. The objective of this study was to understand the epidemiology of HBV infection among the visitors of hospitals in the Jashore region of Bangladesh. Methods A retrospective type of descriptive study has been conducted among 555 patients from October 2017 to August 2018 in Jashore, Bangladesh. Results The overall prevalence of hepatitis B surface antigen (HBsAg) seropositivity was 11% in the total sample population. The number of HBV-infected individuals was greater among males compared to females (15% vs 9%, P-value =0.018). The study found that HBV infection was more prevalent among unmarried individuals (15%) compared to those who were single (10%). Participants age ranging from 29-33 years showed highest HBV prevalence (P-value 0.007). After stratifying by sex, only male patients showed significant difference of blood group (P-value 0.019) and age (P-value 0.001) associated with HBV prevalence. Months of sample collection did not show any significant association with HBV prevalence. Conclusion Our study revealed a higher prevalence of active HBV infection among hospital visitors in Jashore City, especially among males, and those in the younger adult age groups.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".