P90 Reducing inequalities in hepatocellular carcinoma screening in the chronic hepatitis B population – a quality improvement project
Bibliographic record
Abstract
Aims We audited the requests for ultrasound surveillance scans for hepatocellular carcinoma (HCC) in chronic hepatitis B (cHBV) patients at a liver centre in the UK. We aimed to audit adherence to screening criteria and non-attendance rates among patients referred for a scan. This is a quality improvement project with two aims: a) To reduce the costs of inappropriate screening requests by providing education to clinicians, and b) To improve retention of high-risk patients who meet criteria but do not attend through a multi-disciplinary and inclusive approach. Methods We took a retrospective sample of HCC screening ultrasound requests from 28/01–28/04/2023, removing those ordered for non-cHBV patients or cancelled by the hospital. We collected demographic, medical and biochemical data for patients from our electronic patient record. We then calculated whether these patients met screening criteria. Our criteria for 6 monthly HCC ultrasound scans are any of: - Cirrhosis - Family history of HCC - African men >40yrs/women >50yrs - Pan-Pacific Asian with mREACH-B >/=6 - Caucasian with PAGE-B >/=10 We also examined the subsets of patients who did and did not attend to look for any differences between groups. Results A total of 205 HCC screening ultrasound requests were made over a 3 month pilot period. Of these, 92 were not attended (45%). A summary of our results can be seen in table 1. Discussion A screening ultrasound costs around £120/scan, not including cost to patients in terms of travel, missed work and anxiety. Extrapolating our data out to 12 months, adherence to our screening criteria and improving attendance rates could save well over £100,000 per annum at our site alone. Reducing wastage is also key to sustainable practice in hepatology in the UK. Following this pilot study, we will extend our data to 12 months back, and will provide re-education to the team on screening criteria followed by re-audit. HBV is an oncological virus whose prognosis correlates with stage at detection.1 It is notable that nearly two-thirds of non-attenders meet screening criteria, and are therefore at high risk for developing HCC. From the data we have collected, there are no significant differences between these groups (aside from non-attenders are less likely to be of Asian ethnicity), but we plan to use our patient peer supporters to collect qualitiative data from non-attenders on what barriers exist to attendance, and how we can address them to reduce inequalities in provision of screening. Reference Zhu Y, et al. “Long-Term Survival and Risk Factors in Patients with Hepatitis B-Related Hepatocellular Carcinoma: A Real-World Study”, Canadian Journal of Gastroenterology and Hepatology, 2022
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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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".