Impact of an Automated Population-Level Cirrhosis Screening Program Using Common Pathology Tests on Rates of Cirrhosis Diagnosis and Linkage to Specialist Care (CAPRISE): Protocol for a Pilot Prospective Single-Arm Intervention Study
Bibliographic record
Abstract
BACKGROUND: People with compensated cirrhosis receive the greatest benefit from risk factor modification and prevention programs to reduce liver decompensation and improve early liver cancer detection. Blood-based liver fibrosis algorithms such as the Aspartate Transaminase-to-Platelet Ratio Index (APRI) and Fibrosis-4 (FIB-4) index are calculated using routinely ordered blood tests and are effective screening tests to exclude cirrhosis in people with chronic liver disease, triaging the need for further investigations to confirm cirrhosis and linkage to specialist care. OBJECTIVE: This pilot study aims to evaluate the impact of a population screening program for liver cirrhosis (CAPRISE [Cirrhosis Automated APRI and FIB-4 Screening Evaluation]), which uses automated APRI and FIB-4 calculation and reporting on routinely ordered blood tests, on monthly rates of referral for transient elastography, cirrhosis diagnosis, and linkage to specialist care. METHODS: We have partnered with a large pathology service in Victoria, Australia, to pilot a population-level liver cirrhosis screening package, which comprises (1) automated calculation and reporting of APRI and FIB-4 on routinely ordered blood tests; (2) provision of brief information about liver cirrhosis; and (3) a web link for transient elastography referral. APRI and FIB-4 will be prospectively calculated on all community-ordered pathology results in adults attending a single pathology service. This single-center, prospective, single-arm, pre-post study will compare the monthly rates of transient elastography (FibroScan) referral, liver cirrhosis diagnosis, and the proportion linked to specialist care in the 6 months after intervention to the 6 months prior to the intervention. RESULTS: As of January 2024, in the preintervention phase of this study, a total of 120,972 tests were performed by the laboratory. Of these tests, 78,947 (65.3%) tests were excluded, with the remaining 42,025 (34.7%) tests on 37,872 individuals meeting inclusion criteria with APRI and FIB-4 being able to be calculated. Of these 42,025 tests, 1.3% (n=531) had elevated APRI>1 occurring in 446 individuals, and 2.3% (n=985) had elevated FIB-4>2.67 occurring in 816 individuals. Linking these data with FibroScan referral and appointment attendance is ongoing and will continue during the intervention phase, which is expected to commence on February 1, 2024. CONCLUSIONS: We will determine the feasibility and effectiveness of automated APRI and FIB-4 reporting on the monthly rate of transient elastography referrals, liver cirrhosis diagnosis, and linkage to specialist care. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12623000295640; https://tinyurl.com/58dv9ypp. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56607.
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 | 0.046 | 0.037 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".