Data set for the reporting of intrahepatic cholangiocarcinoma, perihilar cholangiocarcinoma and hepatocellular carcinoma: recommendations from the International Collaboration on Cancer Reporting (ICCR)
Bibliographic record
Abstract
Optimal patient management benefits from comprehensive and accurate pathology reports that contribute to cancer staging and prognostication. Proforma reports are used in many countries, but these vary in their structure and implementation. The International Collaboration on Cancer Reporting (ICCR) is an alliance formed by the Royal College of Pathologists of Australasia, the Royal College of Pathologists of the United Kingdom, the College of American Pathologists, the Canadian Partnership Against Cancer the European Society of Pathology and the American Society of Clinical Pathology (ASCP), with the aim of developing an evidence-based reporting data set for each cancer site. It is argued that this should reduce the global burden of cancer data set development and reduplication of effort by different international institutions that commission, publish and maintain standardised cancer reporting data sets. The resultant standardisation of cancer reporting will benefit not only those countries directly involved in the collaboration but also others not in a position to develop their own data sets. We describe the development of a cancer data set by the ICCR expert panel for the reporting of the main malignant liver tumours: intrahepatic cholangiocarcinoma, perihilar cholangiocarcinoma and hepatocellular carcinoma and present the 'required' and 'recommended' elements to be included in the report with an explanatory commentary. This data set incorporates definitions and classifications in the most recent World Health Organisation (WHO) publication on hepatic malignancies (4th edition) and the recently published tumour-node-metastasis (TNM)8 staging system. Widespread adoption and implementation of this data set will enable consistent and accurate data collection, comparison of epidemiological and pathological parameters between different populations, facilitate research and ultimately result in better patient outcomes.
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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 | 0.301 | 0.419 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.014 | 0.010 |
| Research integrity | 0.011 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".