A molecular phylogenetics-based approach for identifying recent hepatitis C virus transmission events
Bibliographic record
Abstract
UNLABELLED: Improved surveillance methods are needed to better understand the current hepatitis C virus (HCV) disease burden and to monitor the impact of prevention and treatment interventions on HCV transmission dynamics. Sanger sequencing (HCV NS5B, HVR1 and Core-E1-HVR1) and phylogenetics were applied to samples from individuals diagnosed with HCV in British Columbia, Canada in 2011. This included individuals with two or three sequential samples collected <1 year apart. Patristic distances between sequential samples were used to set cutoffs to identify recent transmission clusters. Factors associated with transmission clustering were analyzed using logistic regression. From 618 individuals, 646 sequences were obtained. Depending on the cutoff used, 63 (10%) to 92 (15%) unique individuals were identified within transmission clusters of predicted recent origin. Clustered individuals were more likely to be <40 years old (Adjusted Odds Ratio (AOR) 2.12, 95% CI 1.21-3.73), infected with genotype 1a (AOR 6.60, 95% CI 1.98-41.0), and to be seroconverters with estimated infection duration of <1 year (AOR 3.13, 95% CI 1.29-7.36) or >1 year (AOR 2.19, 95% CI 1.22-3.97). CONCLUSION: Systematic application of molecular phylogenetics may be used to enhance traditional surveillance methods through identification of recent transmission clusters.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".