Chronic hepatitis C infection: Prevalance and effect on clinical status of hemodialysis patients in our center
Bibliographic record
Abstract
Hepatitis C virus (HCV) infection affects survival and morbidity of end‐stage renal disease patients and also increases treatment costs. Aim of this study is to define prevalence of HCV infection in our hemodialysis (HD) units and documenting past interventions and clinical status of patients. 711 patients were included. Patient data were collected from 4 HD units of Baskent University. Patient records were examined for demograpic findings, anti‐HCV, HCV RNA, liver biopsy, interferon treatment information, last 12 months’ laboratory values (serum transaminases, albumin, lipid profiles, hemoglobin, C‐reactive protein), and current clinical status, retrospectively. 143 patients (3%) were anti‐HCV positive. These patients’ alanine transferase levels (23.5 ± 20.5 vs 15.5 ± 12.8 U/L, p < 0.0001) and HD duration (97.9 ± 58.6 vs 46.3 ± 35.6 months, p < 0.0001) were significantly higher. No other significant difference could be identified between the groups. Analysis of HD duration of anti‐HCV positive patients revealed that prevalance was increasing as the duration increased. Anti‐HCV positivity was known for a mean of 60.4 ± 38.8 months while 44 patients (30.7%) were already infected at initiation of HD. HCV RNA analysis was positive in 26.7% and a liver biopsy was performed in 23% of patients after 19.8 ± 23.3 months following positive anti‐HCV identification. Minimally active chronic hepatitis C was the most common pathological diagnosis (70, 3%). Only 18 patients (12.5%) recieved interferon therapy. None of the patients had chronic liver disease clinical and physical findings by the time this study was done. HCV infection is a common problem in HD patients that increases the need for medical interventions and treatment.
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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 | 0.002 |
| 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 | 0.001 |
| 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".