Impact of Hispanic or Asian Ethnicity on the Treatment Outcomes of Chronic Hepatitis C
Bibliographic record
Abstract
BACKGROUND AND AIMS: African American ethnicity is a well-described negative predictor of treatment outcome for chronic hepatitis C (CHC); however, less is known about the influence of Hispanic and Asian ethnicity. The aim of this subanalysis of the Weight-based Dosing of PegINterferon α-2b and Ribavirin (WIN-R) study was to assess the impact of Asian (n=118), Hispanic (n=289), and white (n=3919) ethnicity on CHC treatment outcomes. METHODS: WIN-R was an investigator-initiated trial in which patients with CHC received pegylated interferon α-2b (1.5 μg/kg/wk) plus a fixed ribavirin dose (800 mg/d) or a weight-based ribavirin dose (800 to 1400 mg/d) for 24 or 48 weeks. RESULTS: Sustained virologic response was higher in Asian patients than in white patients (56% vs 46%, P=0.041), and higher in Asian and white patients than in Hispanic patients (56% vs 35%, P=0.0001; and 46% vs 35%, P=0.0002, respectively). In genotype 1 patients, sustained virologic response was higher in white and Asian patients than in Hispanic patients (36% and 45% vs 25%, P<0.001 for both comparisons); however, in genotype 2/3 patients, there were no significant differences among ethnic groups. Psychiatric adverse events were less common and anemia was more common in Asians than in white or Hispanic patients. Ribavirin dose reductions were less frequent in Hispanic patients than in white patients, whereas pegylated interferonα-2b dose reductions were more common in white patients than Hispanic patients. CONCLUSION: These observations highlight the importance of ethnicity as an integral component of the tailored treatment approach to CHC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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 teacher head, 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".