Derivation and validation of a risk classification tree for patients with synovial sarcoma
Bibliographic record
Abstract
BACKGROUND: Synovial sarcoma (SS) accounts for 8%-10% of all soft-tissue sarcomas. Clinical presentation and outcomes vary, yet discrete risk groups based on validated prognostic indices are not defined for the full spectrum of patients with SS. METHODS: We performed a retrospective cohort study using data from the SEER (surveillance, epidemiology, and end results program) database of SS patients who were <70 years of age at diagnosis. We constructed a recursive partitioning model of overall survival using a training cohort of 1063 patients with variables: Age at diagnosis, sex, race, ethnicity, primary site, tumor size, tumor grade, and stage. Based on this model, we grouped patients into three risk groups and estimated 5-year overall survival for each group. We then applied these groups to a test cohort (n = 1063). RESULTS: Our model identified three prognostic groups with significantly different overall survival: low risk (local/regional stage with either <21 years of age OR tumor <7.5 cm and female sex), intermediate-risk (local/regional stage, age ≥ 21 years with either male sex and tumor <7.5 cm OR any sex with appendicular anatomic location) and high risk (local/regional stage, age ≥ 21 years, tumor size ≥7.5 cm and non-appendicular location OR distant stage). Prognostic groups were applied to the test cohort, showing significantly different survival between groups (p < 0.0001). CONCLUSIONS: Our analysis yields an intuitive risk-classification tree with discrete groups, which may provide useful information for researchers, patients, and clinicians. Prospective validation of this model may inform efforts at risk-stratifying treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".