Elderly Patients Are at Greater Risk of Cytopenia During Antiviral Therapy for Hepatitis C
Bibliographic record
Abstract
UNLABELLED: The results of antiviral therapy for hepatitis C virus (HCV) have improved recently with the use of pegylated interferon (PEG-IFN)/ribavirin (RBV) combination therapy. At this point, most patients with chronic HCV remain untreated. Thus, it is anticipated that therapy will be more appealing and prescribed more broadly than in the past, including in patients considered marginal. AIM: To examine the effects of PEG-IFN-based antiviral therapy in elderly patients with chronic HCV. METHODS: The charts of patients treated with chronic HCV were reviewed. Patients were defined as elderly if they were 60 years of age or older. The control group consisted of patients younger than 60 years of age who were matched to the treated elderly patients based on sex, treating physician, prescribed treatment and intended prescribed treatment duration. The data recorded included end of treatment response, sustained virological response (SVR), adverse events, dose modification and withdrawal of therapy. RESULTS: Thirty of 147 (20.4%) elderly patients attending a hepatitis C clinic were treated. The average age of the elderly patients was 65+/-4 years. Forty-three per cent were men and 57% were women. Ten per cent received IFN monotherapy, 70% received a combination of IFN/RBV therapy and 20% received a combination of PEG-IFN/RBV therapy. The overall response rates in the elderly patients compared with the younger patients was 46.7% versus 65.8% (P=0.11) for end of treatment response and 33.3% versus 51.2% (P=0.13) for SVR. The rate of dose modification was 50% in the elderly patients compared with 29% in the control group (P=0.08). Therapy was discontinued in 53% of the elderly compared with 34% of younger patients (P=0.17). The younger patients reported more side effects than elderly patients; although, there were more laboratory abnormalities (anemia, thrombocytopenia and neutropenia) in the elderly patients during therapy than in the younger group (0.93 per patient versus 0.49 per patient, P=0.01). CONCLUSION: Elderly patients with chronic HCV can be treated successfully. However, they are more at risk to develop cytopenias while on treatment. In such patients, the close monitoring of blood counts is necessary. Larger studies are needed to confirm these findings and to determine whether SVR differs in this 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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".