Reply
Bibliographic record
Abstract
We thank Drs. Dore and Micallef for their comments on our manuscript discussing the lower risk of HCV reinfection among previously infected injection drug users (IDUs).1 We acknowledge that there are a number of limitations associated with our retrospective study design. They also suggest that the population at risk of reinfection is older and that they may have reduced risk behavior for HCV acquisition, perhaps as a result of their previous diagnosis. Although older, they were using illicit drugs more often than subjects previously uninfected with HCV, suggesting that the risk of HCV acquisition remained high. We acknowledge that with our retrospective design, we were not able to delineate the specific nature of risks associated with drug use (including injection equipment sharing) that would more accurately define HCV transmission risks. However, the extent of the protection we observed makes us confident that there was a protective effect, although measuring the magnitude of this effect would require more reliable risk behavior data and more systematic HCV RNA testing. This relates to their concern regarding the HCV testing frequency. It is indeed possible that we missed some cases of transient reinfection, because our median interval between HCV RNA tests was 15.6 months as compared to just 5 months in the study of Micallef et al.2 Irrespective of these limitations, as mentioned by Dore and Micallef, the absence of chronic reinfection over a long duration of follow-up (5.2 years) in this large study indicates that some IDUs may be protected from HCV reinfection either through reduced risk behaviors for acquisition, host factors responsible for the resolution of primary viremia, or a partial protective immunity leading to enhanced clearance after reinfection. In chimpanzees reinfected with HCV, there is rapid control of viral replication, short-lived viremia, and universal spontaneous resolution of secondary infection.3 Additionally, in humans and chimpanzees, reinfection generally leads to an attenuated course of infection, with the level and duration of viremia markedly reduced.4-7 This being said, our data are quite reassuring in that chronic reinfection seems much less frequent in this population. We are quite intrigued that Dore and Micallef report no protective effect of prior HCV infection in a younger population with more frequent injection drug use.2 It should be pointed out, however, that only 18 individuals at risk of reinfection were evaluated and that the risk of HCV infection in the previously uninfected group was twice that reported by Mehta8 and ourselves,1 suggesting that the protective effect of prior infection may be lower in higher risk subjects. Taken together, these data suggest that the magnitude of protection against reinfection (as well as its specific mechanism) may differ between populations. Future prospective studies are needed to evaluate the natural history of HCV reinfection in IDUs and should include a detailed assessment of risk behaviors and more frequent and systematic HCV RNA testing. This type of information is necessary to better understand the immunopathogenesis and natural history of HCV in IDUs, thereby helping to define public health HCV control measures and treatment recommendations. Jason Grebely*, Brian Conway*, Jesse D. Raffa , Calvin Lai?, Mel Krajden?, Mark W. Tyndall ?, * Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC, Canada, Department of Statistics, University of British Columbia, Vancouver, BC, Canada, Department of Medicine, University of British Columbia, Vancouver, BC, Canada, ? British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada, ? British Columbia Centre for Disease Control, Vancouver, BC, Canada.
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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 | 0.004 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.019 | 0.028 |
| Insufficient payload (model declined to judge) | 0.040 | 0.031 |
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".