Assessment of birth cohort screening of chronic hepatitis C in colorectal cancer screening patients in British Columbia
Bibliographic record
Abstract
Background: Since 2018, British Columbia (BC) has recommended chronic hepatitis C (HCV) screening for those born between 1945 and 1964, with a provincial prevalence of 2.31%. Combining HCV and colorectal cancer (CRC) screening can facilitate specialist referrals and follow-up. We assessed HCV screening uptake among CRC screening patients following the release of BC's birth cohort guidelines and examined the COVID-19 pandemic's impact on HCV screening practices. Methods: A retrospective review was conducted on patients referred to Vancouver Coastal Health Authority's CRC screening program. Two groups, Cohort A (October-December 2019) and Cohort B (December 2021), were studied to identify pandemic-related changes. Data on demographics, liver disease history, hepatitis B or HIV co-infection rates, and initial anti-hepatitis C and ribonucleic acid (RNA) testing dates were collected. Statistical analyses were performed with Stata 15.1. Results: A total of 579 patients were referred for the CRC screening program, of whom 465 were born between 1945 and 1964 and were included in the study. Among the 348 patients in cohort A, 144 (41%, 95% CI 36%-47%) were screened for HCV infection. Of these, four (1.2%) were positive for anti-hepatitis C, and one patient had positive RNA levels. Similar proportions of screenings were observed in cohort B (47.8%, 95% CI 39%-57%). Of those with liver disease, 66% had been screened for HCV. Conclusion: Birth cohort screening for HCV has been underutilized in British Columbia. Combining HCV and CRC screening could provide a practical approach to linking patients to health care.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".