Multiple Soft Tissue Sarcomas in a Single Patient: An International Multicentre Review
Bibliographic record
Abstract
Developing multiple soft tissue sarcomas (STSs) is a rare process, sparsely reported in the literature to date. Little is known about the pattern of disease development or outcomes in these patients. Patients were identified from three tertiary orthopaedic oncology centres in Canada and the UK. Patients who developed multiple extremity STSs were collated retrospectively from prospective oncology databases. A literature review using MEDLINE was also performed. Six patients were identified in the case series from these three institutions, and five studies were identified from the literature review. Overall, 17 patients were identified with a median age of 51 years (range: 19 to 77). The prevalence of this manifestation in STS patients is 1 in 1225. The median disease-free interval between diagnoses was 2.3 years (range: 0 to 19 years). Most patients developed the secondary STS in a metachronous pattern, the remaining, synchronously. The median survival after the first sarcoma was 6 years, and it was 1.6 years after the second sarcoma. The 5-year overall survival rate was 83.3% and 50% following the first and second STS diagnoses, respectively. A diagnosis of two STSs does not confer a worse prognosis than the diagnosis of a single STS. Developing a second STS is a rare event with no identifiable histological pattern of occurrence. Presentation in a metachronous pattern is more common. A high degree of vigilance is required in patients with a previous STS both to detect both local recurrence and to identify new masses remote from the previous STS site. Acquiring an early histological diagnosis should be attempted.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".