Effect Of Hepatitis C Virus On Haemoglobin And Haematocrit Levels In Abuth And Akth Haemodialysis Patients
Bibliographic record
Abstract
Persistent infection with Hepatitis c virus (HCV) has emerged as one of the primary causes of chronic liver disease with an estimated 170 million people infected by HCV, more than 4 times the number of people living with HIV throughout the world. Infection with Hepatitis c virus is common among patients undergoing haemodialysis, and haemodialysis patients are at high risk for infection with such virus. The aim of this study was to assess the seroprevalence and the effect of HCV on PCV and HB in haemodialysis patients who were consented. Hepatitis c virus antibody testing was carried out using CLINOTECH DIAGNOSTIC AND PHARMACEUTICAL INC. B.C. V7A 5H5, CANADA via antibody testing kit. Information about the patient demographic factors and other variables were obtained from the patients or caregivers using a designed questionnaire. A total of 88 blood samples were analysed. The overall seroprevalence rate for HCV was 7.9%. Prevalence of HCV antibody was 6.8% in males and 1.1% in females. The age group of 61-70 years has the lowest prevalence of 1.1% while those of 51-60 years had the highest value of 4.5%. In view of the prevalence rate of HCV infection in this study, it is suggested that further epidemiological studies should be conducted to establish the exact role of HCV in liver disease among haemodialysis patients in Nigeria.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".