Data set for reporting of peripheral neuroblastic tumours: recommendations from the international collaboration on cancer reporting (<scp>ICCR</scp>)
Bibliographic record
Abstract
Peripheral neuroblastic tumours are the most common extracranial solid neoplasms occurring in children. Proper classification is critical for directing therapy and predicting prognosis. Nonetheless, their relative rarity makes accurate pathological assessment challenging, even for experienced pathologists. Here we report on a new international data set for the pathology reporting of biopsy and resection specimens with peripheral neuroblastic tumours. The data set was produced under the auspices of the International Collaboration on Cancer Reporting (ICCR), a global alliance of major (inter-)national pathology and cancer organisations. According to the ICCR's process for data set development, an international expert panel consisting of paediatric pathologists and oncologists produced a set of core and non-core data items for biopsy and resection specimens based on a critical review and discussion of current evidence. All professionals involved were neuroblastic tumour experts affiliated with tertiary referral centres. Commentary was provided for each data item to explain the rationale for selecting it as a core or non-core element, its clinical relevance and to highlight potential areas of disagreement or lack of evidence, in which case a consensus position was formulated. Following international public consultation, the documents were finalised and ratified, and the data sets, including a synoptic reporting guide, were published on the ICCR website. This first international data set for paediatric peripheral neuroblastic tumours is intended to promote high-quality, standardised pathology reporting. Its widespread adoption will improve the consistency of reporting, facilitate multidisciplinary communication and enhance comparability of data, all of which will help to improve management of children with peripheral neuroblastic tumours.
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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 | 0.549 | 0.698 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.035 | 0.026 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.020 | 0.021 |
| Research integrity | 0.015 | 0.029 |
| Insufficient payload (model declined to judge) | 0.011 | 0.016 |
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".