Datasets for reporting of soft‐tissue sarcoma: recommendations from the International Collaboration on Cancer Reporting (<scp>ICCR</scp>)
Bibliographic record
Abstract
AIMS: Soft-tissue tumours are rare and both accurate diagnosis and proper treatment represent a global challenge. Current treatment guidelines also recommend review by specialised pathologists. Here we report on international consensus-based datasets for the pathology reporting of biopsy and resection specimens of soft-tissue sarcomas. The datasets were produced under the auspices of the International Collaboration on Cancer Reporting (ICCR), a global alliance of international pathology and cancer organisations. METHODS AND RESULTS: According to the ICCR's guidelines for dataset development, an international expert panel consisting of pathologists, a surgical oncologist, and a medical oncologist produced a set of core and noncore data items for biopsy and resection specimens based on a critical review and discussion of current evidence. All professionals involved were subspecialised soft-tissue sarcoma experts and affiliated with tertiary referral centres. Commentary was provided for each data item to explain the rationale for selecting it as a core or noncore 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 datasets, which included a synoptic reporting guide, were published on the ICCR website. CONCLUSION: These first international datasets for soft-tissue sarcomas are aimed to promote high-quality, standardised pathology reporting. Their adoption will improve consistency of reporting, facilitate multidisciplinary communication, and enhance comparability of data, all of which will help to improve patient's management.
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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 | 0.633 | 0.747 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.032 | 0.037 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.022 | 0.027 |
| Research integrity | 0.014 | 0.025 |
| Insufficient payload (model declined to judge) | 0.013 | 0.017 |
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".