Data set for the reporting of intrahepatic cholangiocarcinoma, perihilar cholangiocarcinoma and hepatocellular carcinoma: recommendations from the International Collaboration on Cancer Reporting (ICCR)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it