Plasma interferon-gamma-inducible protein-10 (IP-10) levels during acute hepatitis C virus infection
Bibliographic record
Abstract
UNLABELLED: Systemic levels of interferon-gamma-inducible protein-10 (IP-10) are predictive of treatment-induced clearance in chronic hepatitis C virus (HCV). In the present study, factors associated with plasma IP-10 levels at the time of acute HCV detection and the association between IP-10 levels and spontaneous clearance were assessed in three cohorts of acute HCV infection. Among 299 individuals, 245 (181 male, 47 human immunodeficiency virus-positive [HIV+]) were HCV RNA+ at acute HCV detection. In adjusted analysis, factors independently associated with IP-10 levels ≥150 pg/mL (median level) included HCV RNA levels >6 log IU/mL, HIV coinfection and non-Aboriginal ethnicity. Among 245 HCV RNA+ at acute HCV detection, 214 were untreated (n = 137) or had persistent infection (infection duration ≥26 weeks) at treatment initiation (n = 77). Spontaneous clearance occurred in 14% (29 of 214). Individuals without spontaneous clearance had significantly higher mean plasma IP-10 levels at the time of acute HCV detection than those with clearance (248 ± 32 versus 142 ± 22 pg/mL, P = 0.008). The proportion of individuals with spontaneous clearance was 0% (0 of 22, P = 0.048) and 16% (27 of 165) and in those with and without plasma IP-10 levels ≥380 pg/mL. In adjusted analyses, favorable IL28B genotype was associated with spontaneous clearance, while higher HCV RNA level was independently associated with lower odds of spontaneous clearance. CONCLUSION: High IP-10 levels at acute HCV detection were associated with failure to spontaneously clear HCV. Patients with acute HCV and high baseline IP-10 levels, particularly >380 pg/mL, should be considered for early therapeutic intervention, and those with low levels should defer therapy for potential spontaneous clearance. (HEPATOLOGY 2013;).
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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".