The More You Look, the More You Find: Effects of Hepatitis C Virus Testing Interval on Reinfection Incidence and Clearance and Implications for Future Vaccine Study Design
Bibliographic record
Abstract
INTRODUCTION: Studies have explored whether spontaneous clearance of hepatitis C virus (HCV) infection decreases the likelihood of reinfection or increases the probability of clearance. This analysis investigates whether the conflicting findings from these studies could be due to differences in frequency of HCV RNA testing. METHODS: A model simulated the dynamics of HCV reinfection and clearance among a cohort of injection drug users. For different reinfection incidence and clearance rates, the model evaluated the accuracy of epidemiological studies that used different HCV testing frequencies. RESULTS: Experimental estimates for the reinfection incidence and clearance probability will be accurate (<20% error) if the testing interval is less than the reinfection clearance duration. Otherwise, experimental estimates can greatly underestimate the real values (≤66% error if reinfection duration is 1 month and the testing interval is 3 months). Uncertainty in experimental estimates also increases at lower reinfection incidences, whereas for lower clearance probabilities the uncertainty in the estimated clearance probability increases but estimated reinfection incidence decreases. DISCUSSION: Differences in HCV testing interval could account for most between-study variability in the estimated probability of clearing reinfections and is likely to have biased reinfection incidence estimates. Our findings suggest that a high reinfection clearance probability (>75%) is consistent with data.
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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 | 0.139 | 0.344 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".