Familial sarcoma
Bibliographic record
Abstract
BACKGROUND: Partially due to the rare occurrence of soft tissue and osteogenic sarcomas in the general population, scant attention has been given to their hereditary etiology. Their overall poor prognosis might be ameliorated through an understanding of their environmental and hereditary causal factors, and/or their interactions, thereby contributing to earlier diagnosis and even the development of molecularly based targeted therapy. METHODS: The authors selected 10 sarcoma-prone families from their extensive hereditary cancer-prone family resource and focused on their challenging diagnostic, surveillance, and management features. The family study protocol included the compilation of a detailed family history of malignant disease of all anatomic sites and the collection of all available primary medical and pathology documents for verification. Genetic counseling was provided before DNA collection and at disclosure of results. RESULTS: These families displayed marked phenotypic and genotypic heterogeneity. In one of these families, 16 relatives had sarcomas, with 2 of the 16 each having 2 metachronous sarcomas; to our knowledge, this represents the greatest number of sarcomas reported in any family described to date. Two familial atypical multiple-mole melanoma syndrome kindreds with the CDKN2A mutation showed the association of sarcoma with malignant melanoma, whereas one family had several pancreatic carcinomas. Other families with sarcoma had hereditary nonpolyposis colorectal carcinoma with MSH2 mutation, hereditary breast carcinoma with BRCA1 mutation, and p53 mutation in a Li-Fraumeni syndrome. CONCLUSIONS: Sarcoma-prone families reported in the current study were selected carefully to depict clinicopathology and compliance features, the understanding of which could elucidate the etiologic role of genetic factors in concert with the phenotypic and genotypic heterogeneity encountered in such families. The lack of a population-based data set for these families posed a limitation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.013 |
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".