Prospective cohort for early detection of liver cancer (Pearl): a study protocol
Bibliographic record
Abstract
INTRODUCTION: Hepatocellular carcinoma (HCC) is the fastest-rising and fourth most common cause of cancer death worldwide. Liver cirrhosis is the largest underlying risk factor for HCC. Therefore, patients with cirrhosis should have regular ultrasound and biochemical screening to pick up early HCC. Early HCC can be cured; more advanced HCCs have limited treatment options and poor prognosis. Current screening methods are suboptimal with poor sensitivity in picking up early disease. In this study, the investigators aim to recruit people with liver cirrhosis into a Prospective cohort for early detection of liver cancer-the Pearl cohort. The investigators believe that by using state-of-the-art tests we can improve the detection of early HCC. METHODS AND ANALYSIS: This is a UK-based prospective, longitudinal, diagnostic, prognostic, multicentre, non-CTIMP study. Aiming to recruit 3000 patients with liver cirrhosis without a HCC diagnosis, the Pearl cohort will be followed actively for 3 years from recruitment and then passively via registry data for ten years thereafter. Blood and urine samples will be taken and information from routine care will be gathered. These will be used to assess novel diagnostic approaches for the detection early HCC and to develop models to identify those most at risk for developing HCC.Participants will be linked to national UK health registries to ensure long-term capture of HCC incidence and other relevant endpoints. Approximately 75 patients are predicted to develop de novo HCC within the 3-year follow up period. After this period, the study teams will obtain data on participants for at least 10 years after the last contact. This cohort will help develop an understanding of the incidence of HCC in a UK population stratified by underlying cirrhosis aetiology. ETHICS AND DISSEMINATION: Ethical approval has been granted by REC and the trial is registered on ClinicalTrials.gov. The results will be published in peer-reviewed journals and presented at relevant meetings. TRIAL REGISTRATION NUMBER: NCT05541601.
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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 | 0.029 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.059 | 0.020 |
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".