Effect of viral eradication with direct-acting antiviral agents on iron parameters in patients with chronic hepatitis c and hyperferritinemia
Bibliographic record
Abstract
Background: Patients with chronic hepatitis C are at increased risk for hyperferritinemia (HF). Abnormalities of serum iron parameters are frequently observed in patients with chronic hepatitis C (CHC). About a third of patients have increased iron parameters. Recently, studies on the effect of direct-acting antiviral agents (DAAs) in HCV eradication in patients with increased serum iron has been published, demonstrating the restoration of normal iron status. The aim of this study was to evaluate the effect of viral eradication with DDAs in patients with CHC and HF. Methods: Retrospective study conducted from January 2018 to December 2020 including patients treated with DAAs for HCV. Pre-treatment (PreT) and post-treatment (PostT) serum ferritin values were evaluated in all patients. Inclusion criteria: Pret HF (>400 μg/L); CHC patients treated with DAA achieving sustained viral response (SVR). Exclusion criteria: No PreT or PostT HF available; no SVR; lost patients. Results: From 621 patients treated with DAAs for CHC, 77 presented HF (12.40%), and 74 were included in the study. Fifty nine were men (79.73%) with a mean age 58.33, SD 8.68; PreT mean ferritin: 893.20 (SD 1037.09); PostT: 264.17 (SD 161.33); PreT mean transferrin saturation: 40.96 (SD 15.71); PostT: 29.82 (SD 11.17); PreT mean serum iron 152.32 (SD 62.07), PostT: 109.32 (SD 39.49). When we compared PreT and PostT iron parameters, significant statistical differences were present considering ferritin ( p = 0.0000), transferrin saturation ( p = 0.0000), and iron ( p = 0.0002) determinations. Conclusions: SVR after DAAs for CHC induces a statistically significant reduction on iron parameters.
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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 | 0.000 | 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 | 0.000 |
| 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".