Clinical care in hepatocellular carcinoma: A mixed methods assessment of experiences and challenges of oncology professionals
Bibliographic record
Abstract
INTRODUCTION: Healthcare providers (HCPs) may face numerous dilemmas in optimally screening, diagnosing, and treating patients with, and/or at risk for, hepatocellular carcinoma (HCC). This study aimed to achieve a greater understanding of the challenges in HCC care which in turn could delineate HCP educational opportunities within this oncologic sub-specialty. METHODS: A mixed-methods approach was used to identify practice gaps and clinical barriers experienced by US-based medical oncologists, hepatologists, oncology physician assistants, oncology nurse practitioners, and interventional radiologists involved in HCC care. The qualitative (semi-structured interview) and quantitative (survey) data collection approaches were deployed sequentially with findings subsequently triangulated. RESULTS: A total of 214 HCPs participated in this study. Analysis revealed challenges related to screening and diagnosing HCC, specifically in applying appropriate screening guidelines, and the optimal use and decisions related to diagnostic imaging and biopsy. Issues related to treatment selection included the application of existing HCC guidelines in treatment decision-making, weighing risk/benefit ratios of various antineoplastics regimens (i.e., tyrosine kinase inhibitors-TKIs, immunotherapy agents, chemotherapy), sequencing therapies, potential toxicity management, and optimally educating patients about their HCC. CONCLUSION: These findings highlight the educational needs of those involved in HCC care and provide a starting point for clinicians to both reflect on their practice and identify opportunities to enhance communication within the HCC team and between provider and patient. There is an opportunity to optimize continuing professional development interventions that address the identified gaps in clinical practice specifically related to teamwork and interdisciplinary communication.
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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 | 0.039 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".