Hepatitis B and Hepatitis C Virus Infection among End Stage Renal Disease Patients on Maintenance Hemodialysis, Their Family Members and Dialysis Staffs - A Prevalence Study
Bibliographic record
Abstract
Background: Hepatitis B (HBV) and hepatitis C virus (HCV) infection is common among patients on maintenance hemodialysis (MHD). This study was undertaken to observe prevalence of hepatitis B and C infection in hemodialysis patients, their family members and dialysis staffs.Methods: In this cross-sectional study 3 groups were included as gr-1 patients on MHD, the first-degree relatives in gr-2 and the dialysis staffs as gr-3. Clinical and laboratory investigations were done. Viral serology included hepatitis B surface antigen (HBsAg) and antibody against hepatitis C virus (anti-HCV) done by enzyme linked immunosorbent sorbent assay (ELISA) method.Results: Total 150 subjects were analyzed where 50 were in gr-1, 60 gr-2 and 40 in gr-3. In gr-1 MHD patients, HBV infection was positive in 12% and HCV in 71%. None of the viral markers were positive in family members and dialysis staffs. Blood transfusion (BT) was taken by 76% MHD patents. The frequency of HBV and HCV infection was of similar pattern in both BT dependent and non BT group (P=NS).When HCV positive and negative patents were (71 vs. 29%, p<0.001) compared, dialysis duration (37 ± 34 vs. 11± 6 months, p<0.001) was found higher in positive patients.Conclusion: This survey on dialysis patients showed that prevalence of hepatitis B and C virus infections was higher in Bangladeshi patients on MHD groups. Horizontal spread of these viruses is negligible in caregivers and dialysis staffs.Birdem Med J 2018; 8(1): 42-46
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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) | 0.001 | 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".