Conference on challenges in sarcoma (CCS) 2024: Expert opinions on non-evidence-based management aspects
Bibliographic record
Abstract
BACKGROUND: Soft tissue sarcomas (STS) and other mesenchymal tumours belong to rare, heterogeneous neoplasms with over 150 subtypes that pose significant challenges in diagnosis and clinical decision making. While guidelines address evidence-based diagnostic and therapeutic procedures, clinical situations and scenarios without evidence remain controversial in daily practice. The 2024 Conference on Challenges in Sarcoma (CCS2024) aimed to narrow these gaps with the support of an international and multidisciplinary panel of sarcoma experts. METHODS: A Delphi process identified 200 controversial questions across eight prioritised clinical scenarios, including tenosynovial giant cell tumour, synovial sarcoma of the extremities, retroperitoneal sarcomas, angiosarcoma, phyllodes tumour, malignant peripheral nerve sheath tumour, uterine leiomyosarcoma, and atypical lipomatous tumour. RESULTS: Sixty-four experts discussed 141 controversies during the conference and reached strong consensus (> 90 %) on 24 and consensus (> 75 %) on 45 key diagnostic and therapeutic issues, while unresolved controversies emphasized the need for further research. CONCLUSIONS: CCS2024 provides a framework for clinical decision making and underscores the importance of consensus-driven approaches in the treatment of rare and complex malignancies.
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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 | 0.125 | 0.203 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".