Seroprevalence of hepatitis b surface antigen (HBsAg) among the medicalstudents of Usmanu Danfodiyo University, Sokoto, Sokoto State, Nigeria
Bibliographic record
Abstract
Hepatitis B virus infection is a salient occupational hazard for health workers. It has been estimated that about two billion people worldwide have been infected with the virus. It is the 10th leading cause of death worldwide and results in 500,000 to 1.2million deaths per year due to cirrhosis and hepatocellular carcinoma. The presence of HBsAg in serum or plasma is an indication of active Hepatitis B infection, either acute or chronic. Healthcare workers, of which medical students are a part of, are at high risk of encountering accidental needle prick injuries, blood and body fluid exposure and hence acquiring blood borne infections, especially Hepatitis B and C, which may be followed by serious long term sequelae in a significant number of cases. The aim of this study was to determine the seroprevalence rate of Hepatitis B surface antigen (HBsAg) among the medical students of Usmanu Danfodiyo University, attending Usmanu Danfodiyo University Teaching Hospital (UDUTH), Sokoto. 245 medical students participated in this study, qualitative detection of HBsAg was done using one step HBsAg rapid test strips (DiaSpot Diagnostics, USA) and (Clinotech Diagnostics, Richmond, Canada). Results were correlated and reported as positive or negative. Among the 245 samples analyzed, 38samples were positive for HBsAg accounting for a prevalence rate of 15.5%. This prevalence was however statistically non – significant (pA‹Âƒ0.05). The major risk factors of hepatitis B transmission among medical students include unprotected exposure body fluids, blood and blood products as well as lack of vaccination.
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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 | 0.001 | 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) | 0.002 | 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".