Impact of iatrogenic iron overload on the course of hepatitis C in the dialysis population: A plea for caution
Bibliographic record
Abstract
About 2.5% of the world population, corresponding to about 177 million individuals, are infected by hepatitis C virus (HCV), a small, single-stranded RNA virus. The prevalence of HCV infection among dialysis patients in Japan, Europe, and North America during the 2012 to 2015 period was found to be 8.7% in the DOPPS study. Nosocomial HCV spread in hemodialysis facilities still occurs. Increased hepatic tissue iron has been shown to play a deleterious role in the course of hepatitis C, favor development of fibrosis and cirrhosis and possibly increase the risk of liver cancer in the general population. Regular loss of blood in the hemodialysis circuit, in routine blood sampling for laboratory tests (for uremia monitoring), and in gut due to uremic enteropathy, invariably results in iron deficiency for which patients are commonly treated with intravenous (IV) iron preparations. Data on the effects of IV iron in hemodialysis patients with hepatitis C are limited (2 studies) and strongly suggest that parenteral iron may contribute to hepatocellular injury. Iatrogenic iron overload is extremely prevalent among hemodialysis population worldwide. Iron overload and toxicity has emerged as one of the most controversial topic in the management of anemia in dialysis patients. Given the known impact of iron in promoting growth and virulence of HCV and the associated liver disease, it is necessary to use iron therapy cautiously and closely monitor plasma markers of iron metabolism and liver iron stores non-invasively by means of MRI to avoid iron overload in this vulnerable population.
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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 | 0.022 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".