Ultra‐rare sarcomas: A consensus paper from the Connective Tissue Oncology Society community of experts on the incidence threshold and the list of entities
Bibliographic record
Abstract
BACKGROUND: Among sarcomas, which are rare cancers, many types are exceedingly rare; however, a definition of ultra-rare cancers has not been established. The problem of ultra-rare sarcomas is particularly relevant because they represent unique diseases, and their rarity poses major challenges for diagnosis, understanding disease biology, generating clinical evidence to support new drug development, and achieving formal authorization for novel therapies. METHODS: The Connective Tissue Oncology Society promoted a consensus effort in November 2019 to establish how to define ultra-rare sarcomas through expert consensus and epidemiologic data and to work out a comprehensive list of these diseases. The list of ultra-rare sarcomas was based on the 2020 World Health Organization classification, The incidence rates were estimated using the Information Network on Rare Cancers (RARECARENet) database and NETSARC (the French Sarcoma Network's clinical-pathologic registry). Incidence rates were further validated in collaboration with the Asian cancer registries of Japan, Korea, and Taiwan. RESULTS: It was agreed that the best criterion for a definition of ultra-rare sarcomas would be incidence. Ultra-rare sarcomas were defined as those with an incidence of approximately ≤1 per 1,000,000, to include those entities whose rarity renders them extremely difficult to conduct well powered, prospective clinical studies. On the basis of this threshold, a list of ultra-rare sarcomas was defined, which comprised 56 soft tissue sarcoma types and 21 bone sarcoma types. CONCLUSIONS: Altogether, the incidence of ultra-rare sarcomas accounts for roughly 20% of all soft tissue and bone sarcomas. This confirms that the challenges inherent in ultra-rare sarcomas affect large numbers of patients.
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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 | 0.101 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".