Pathology reporting of hepatoblastoma resections: recommendations from the international collaboration on cancer reporting
Bibliographic record
Abstract
AIMS: Hepatoblastoma is the most common primary malignant tumour of the liver diagnosed in children and its incidence is increasing worldwide. Ongoing international clinical trials and scientific collaborative efforts are attempting to standardize the diagnosis, risk stratification and management of young patients diagnosed with this rare cancer, which includes surgical resection of the tumour. Here we report the international consensus-based dataset for the pathology reporting of hepatoblastoma resection specimens. The dataset was developed under the auspices of the International Collaboration on Cancer Reporting (ICCR), a global alliance of international pathology and cancer organizations. METHODS AND RESULTS: According to the ICCR's guidelines for dataset development, an international expert panel including paediatric pathologists and a paediatric oncologist specialized in liver tumours developed a set of core and non-core data items for hepatoblastoma resection specimens based on critical review and discussion of current evidence available. Members of the panel were specialists working in tertiary paediatric hospitals, central reviewers for international paediatric liver tumours consortia and/or involved in paediatric liver tumour trials expert committees. Commentaries were provided to support each data item, explaining the rationale for selecting them as a 'core' or 'non-core' elements, their clinical relevance and highlighting potential areas of lack of evidence, including clinical, macroscopic, microscopic and ancillary testing considerations. The hepatoblastoma dataset was finalized and ratified following international public consultation and is published on the ICCR website for wide implementation. CONCLUSION: This is the first international dataset developed by an international expert panel for reporting hepatoblastoma resection specimens aimed to promote high-quality, standardized pathology reporting of these rare paediatric liver tumours. The adoption and implementation of this hepatoblastoma data set, freely available worldwide on the ICCR website (www.iccr-cancer.org/data-sets), will facilitate accurate reporting and enhance the consistency of data collection, support retrospective and inform prospective research and ultimately help to improve clinical outcomes of children with hepatoblastoma.
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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 | 0.529 | 0.638 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.027 | 0.029 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.020 | 0.022 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".