Conditional Probabilities of HBV markers among Health Care Workers in Public Hospitals in White Nile State, Sudan; 2013
Bibliographic record
Abstract
BACKGROUND: Health-care workers are having highest probability of being infected with HBV. OBJECTIVE: To determine conditional probability of sero-prevalence of hepatitis B virus markers among health care workers in White Nile State, Sudan. METHODS: A cross sectional study design with analytical approach was used. Three hundred eighty five health care workers were selected randomly. An interview was carried using a pre-tested questionnaire and five ml venous blood samples were consented. Blood samples were tested for Anti-HB core total, HBsAg and HBeAg. Conditional probabilities of being a carrier and highly infective were calculated regarding departments, occupation of HCWs, marital status and working duration in hospital. RESULTS: Out of the total study population, 230 (59.7%) were positive for anti-HB core total. Out of 230 HCWs, 62 (27.1%) were positive for HBsAg. Out of 62 HCWs, 29 (46.8%) were positive for HBeAg. In overall, 16% of study population was carriers and 7.5% were highly infective. HCWs in surgical and Obstetrics & gynaecology had 0.50 conditional probability of being carriers and highly infective. Laboratory technicians had 0.64 conditional probability of being carriers and highly infective. HCWs with working duration in hospitals up to 5 years had 0.63 probability of being carriers and highly infective. CONCLUSION: Prevalence and conditional probabilities of HBV markers among health care worker in White Nile State were high. HCWs in Surgical and Obstetrics & gynaecology, Laboratory technicians and HCWs with working duration up to 5 years are carriers and highly infective. Periodical screening and vaccination of HCWs are recommended.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".