A review of general hepatitis C virus lookbacks in Canada
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This article reviews the Canadian experience with general hepatitis C virus (HCV) lookback programmes. MATERIALS AND METHODS: Comprehensive literature searches were conducted in PubMed, Medline, HealthSTAR and EMBASE. In addition, bibliographic searches were performed on all retrieved articles, and provinces were contacted to determine whether they had performed general HCV lookbacks. RESULTS: Of the seven Canadian general HCV lookbacks identified, two focused specifically on the paediatric population. The proportion of transfused patients presumed to be alive varied from 48.9 to 97.5%. Between 55.3 and 99.1% of letters were successfully delivered. The proportion of patients tested for HCV and subsequently found to be HCV positive varied considerably (66.2-80.4% and 0.9-5.0%, respectively). Newly diagnosed patients represented 42-58% of cases identified. CONCLUSIONS: The Canadian general HCV lookback experience successfully identified previously undiagnosed HCV-positive patients, but the resources required to notify patients are high and the yield is relatively low. The effectiveness may be greatest in the paediatric population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".