Data set for reporting paediatric renal tumours: recommendations from the international collaboration on cancer reporting (<scp>ICCR</scp>)
Bibliographic record
Abstract
Tumours arising within the developing kidney of children vary widely in their histological appearance and outcome; optimal therapy requires accurate classification and staging. The two major paediatric cooperative groups provide different therapeutic protocols based on different staging and classification, initially developed to serve patients in North America and Europe, but also used in many other parts/regions of the world. The International Collaboration on Cancer Reporting (ICCR) has developed a structure whereby such complex information may be harmonised, and able to be applied to patients globally. An international expert panel consisting of paediatric pathologists and oncologists produced a set of items critical to cancer reporting and subjected these to review and discussion using the structured processes provided by the ICCR. A formal ICCR structure was assembled, and consensus surrounding elements and their application to different therapeutic protocols was developed. The data set underwent open international consultation. This resulted in the first international data set for Wilms tumour (WT) and other paediatric renal tumours, provided herein. The use of ICCR methods enables a full understanding of highly complex and often overlapping reporting elements by international experts, and the potential of developing a set of commonly applied data elements that are fully defined. This sets the groundwork for future consolidation of definitions and harmonisation of therapies for WT and other paediatric renal tumour patients. It also allows institutions outside the major paediatric cooperative groups to provide therapy based on known elements.
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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 | 0.335 | 0.445 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.034 | 0.033 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.015 | 0.013 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 0.014 |
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".