Impact of <scp>COVID</scp>‐19 pandemic on hepatocellular carcinoma surveillance in British Columbia, Canada: An interrupted time series study
Bibliographic record
Abstract
We assessed the impact of the COVID-19 pandemic on hepatocellular carcinoma (HCC) surveillance among individuals with HCV diagnosed with cirrhosis in British Columbia (BC), Canada. We used data from the British Columbia Hepatitis Testers Cohort (BC-HTC), including all individuals in the province tested for or diagnosed with HCV from 1 January 1990 to 31 December 2015, to assess HCC surveillance. To analyse the impact of the pandemic on HCC surveillance, we used pre-policy (January 2018 to February 2020) and post-policy (March to December 2020) periods. We conducted interrupted time series (ITS) analysis using a segmented linear regression model and included first-order autocorrelation terms. From January 2018 to December 2020, 6546 HCC screenings were performed among 3429 individuals with HCV and cirrhosis. The ITS model showed an immediate decrease in HCC screenings in March and April 2020, with an overall level change of -71 screenings [95% confidence interval (CI): -105.9, -18.9]. We observed a significant decrease in HCC surveillance among study participants, regardless of HCV treatment status and age group, with the sharpest decrease among untreated HCV patients. A recovery of HCC surveillance followed this decline, reflected in an increasing trend of 7.8 screenings (95% CI: 0.6, 13.5) per month during the post-policy period. There was no level or trend change in the number of individuals diagnosed with HCC. We observed a sharp decline in HCC surveillance among people living with HCV and cirrhosis in BC following the COVID-19 pandemic control measures. HCC screening returned to pre-pandemic levels by mid-2020.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".