An Open-Access, Interactive Decision-Support Tool to Facilitate Guideline-Driven Care for Hepatocellular Carcinoma
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is increasing in incidence and is a leading cause of cancer-related mortality worldwide. Adherence to HCC surveillance guidelines and appropriate treatment triage of liver lesions may improve receipt of curative-intent treatment and improved survival. Late-stage HCC diagnosis reflects sub-optimal implementation of effective HCC surveillance, whereas inappropriate treatment triage or linkage to care accounts for the non-receipt of curative-intent in close to half of early-stage HCC in the USA. A free, open-access decision-support tool for liver lesions that incorporates current guideline recommendations in a user-friendly interface could improve appropriate and timely triage of patients to appropriate care. This review provides a summary of gaps and disparities in linkage to HCC care and introduces a free, internet-based, interactive decision-support tool for managing liver lesions. This tool has been developed by the HCC Steering Committee of the Chronic Liver Disease Foundation and is targeted toward clinicians across specialties who may encounter liver lesions during routine care or as part of dedicated HCC surveillance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".