A global comparative study on MDT practices in the management of hepatocellular carcinoma in high-income countries (HICs) and low- and middle-income countries (LMICs).
Bibliographic record
Abstract
562 Background: As the sixth most common malignancy worldwide, hepatocellular carcinoma (HCC) accounted for 4.7% of all new cancer diagnoses in 2022. Low- and middle-income countries seem to bear a disproportionate burden of the disease, with over 50% of new HCC cases estimated to occur in China and Africa. Research on multidisciplinary team (MDT) practices in these regions is limited. The objective of this study is to compare MDT practices in the management of HCC in HICs and LMICs. Methods: Data on MDT practices from hospitals that manage HCC in Italy, Spain, Switzerland, Germany, Denmark, Canada and the USA, representing HICs, and in China, South Africa and Egypt, representing LMICs were collected through virtual semi-structured interviews and workshops. Results: Ten key areas of differences were identified (Table 1) that could be categorized as organizational/regulatory or clinical. In HIC institutions, the focus currently is on optimizing MDTs as standard of care approach accessible to all patients with HCC; MDT principles are often integrated into national policies. In LMICs, emphasis is on establishing MDTs and expanding their capacity; patient access to MDT care is not a mandatory requirement, nor regulated in national policy. Of note, LMICs are seeking early technology adoption to overcome some potential barriers to MDT interactions; online formats are more often explored to facilitate participation and AI-assisted diagnostic tools are explored to compensate for absence of radiologists. Absence of screening and surveillance for early detection in HCC is more common in LMICs, and therefore likely that patients more often present with advanced stage disease. Also, with limited diagnostic modalities, diagnostic work-up and staging may be sub-optimal. Conclusions: Significant variation exists in HCC MDT practices between HICs and LMICs. The ultimate goal of this study is to develop a format for clinical MDTs that will facilitate interaction between HICs and LMICs. Consideration for the differences highlighted above is crucial for the development of this concept. 10 key areas highlighting differences identified between HICs and LMICs. MDT development direction: Standard of Care for all patients vs. specific session for complex cases National/regional regulatory requirements Institutionalizing MDT principles MDT composition (specialists) Use of digital tools Reimbursement Access to therapeutic options Stage presentation of HCC patients Use of clinical staging Patient follow-up after MDT discussion
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".