Personal risk networks and the molecular epidemiology of Hepatitis C amongst injection drug users in Winnipeg
Bibliographic record
Abstract
Introduction: Despite prevention programs, HCV is still on the rise within the injection dmg use population (IDU).In this, Phase II of the Winnipeg Socìal Network Injection Drug Use study (Wiruipeg SNS IDU), individual data and social network data as well as molecular data r.vere used to examine transmission of hepatitis C. Methods: A public health nurse collected interview data and blood samples from 435 consenting individuals in Winnipeg, who used drugs intravenously during the 6 months preceding the interview date.Blood samples were tested for the presence of antibodies against HCV, and positive specimens were genotyped.Logistic regression analyses were used to correlate data with HCV genotype.Association indices were used to determine the degree of segregation of sequences between study participants, based on key characteristics.Results: Prevalence of HCV in the study population was 54.4o/o.The genotypes circulating in this population were 1a (82,.59.)yo),3a (47,33.8%),2a(3,2.2o/o),2b(2, 1.4%), lb (5, 3.6%).At the multivariate level, HCV genotype 3a was associated with younger age and injecting on the street, when compared to genotype 1a.Moderate segregation of sequences \Ã/as seen for those individuals who had injected in hotels and/or public washrooms, had moved to V/innipeg in the past year andlor had participated in the sextrade.Conclusions: The findings suggest distinct networks of HCV transmission exist in the study population, such that more intra-network connectivity than inter-network connectivity is present.Targeted prevention and treatment strategies should be used in the local IDU population, as some public health messages may not readily diffuse to all groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".