A Patient Charter to Improve Care for Hepatocellular Carcinoma
Bibliographic record
Abstract
Purpose: To establish a patient charter that articulates the principles of quality care for individuals living with hepatocellular carcinoma (HCC), aiming to improve patient outcomes and survival rates globally. Methods: A multidisciplinary group comprising healthcare professionals, patient advocacy representatives, and policymakers convened to identify the critical areas of unmet need in HCC care. The group shared patient experiences, barriers, and insights - particularly with input from Patient Advocacy Groups (PAGs) - to better understand the challenges faced by patients. They reviewed existing literature, current care practices, and patient experiences to formulate a patient charter that outlines the principles of quality care for HCC. Results: The patient charter identifies the seven principles of quality care that people with HCC or at risk of developing HCC should expect to receive in order to benefit from improved outcomes and increased survival. These principles address the need for policy prioritization, early diagnosis, multidisciplinary care, personalized treatment, shared decision-making, stigma-free access to services and increased research funding. Conclusion: The patient charter serves as a call to action for stakeholders to unite in enhancing the care and treatment of HCC, with the ultimate goal of improving health outcomes for patients.
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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 | 0.051 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".