Clinical consensus statement: Selective internal radiation therapy with yttrium 90 resin microspheres for hepatocellular carcinoma in Asia
Bibliographic record
Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) is subject to different management approaches and guidelines according to Eastern and Western therapeutic algorithms. Use of selective internal radiation therapy (SIRT) with resin yttrium 90 microspheres for HCC has increased in Asia in recent years, without clearly defined indications for its optimal application. The objective of this systematic review and expert consensus statement is to provide guidance and perspectives on the use of SIRT among patients with HCC in Asia. MATERIALS AND METHODS: A systematic literature review identified current publications on HCC management and SIRT recommendations. A group of 10 experts, representing stakeholder specialties and countries, convened between August 2020 and March 2021 and implemented a modified Delphi consensus approach to develop guidelines and indications for use of SIRT for HCC in Asia. Final recommendations were organized and adjudicated based on the level of evidence and strength of recommendation, per approaches outlined by the American College of Cardiology/American Heart Association and Oxford Centre for Evidence-Based Medicine. RESULTS: The experts acknowledged a general lack of evidence relating to use of SIRT in Asia and identified as an unmet need the lack of phase 3 randomized trials comparing clinical outcomes and survival following SIRT versus other therapies for HCC. Through an iterative process, the expert group explored areas of clinical relevance and generated 31 guidance statements and a patient management algorithm that achieved consensus. CONCLUSION: These recommendations aim to support clinicians in their decision-making and to help them identify and treat patients with HCC using SIRT in Asia. The recommendations also highlight areas in which further clinical trials are needed to define the role of SIRT in management of HCC among Asian populations.
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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 | 0.085 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.019 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".