A comparison of staging systems for localized extremity soft tissue sarcoma
Bibliographic record
Abstract
BACKGROUND: Staging systems for soft tissue sarcoma (STS) are important to identify patients with similar systemic risk who might benefit from specific treatments. This study compared four commonly used staging systems for predicting systemic outcomes of patients with localized extremity STS, as proposed by the fourth and fifth editions of the American Joint Committee on Cancer/International Union Against Cancer (AJCC/UICC) staging system, the Memorial Sloan-Kettering Cancer Center (MSK) system, and the Surgical Staging System (SSS) of the Musculoskeletal Tumor Society. METHODS: Three hundred consecutive adult patients with newly diagnosed nonmetastatic STS of the lower extremity were treated at Memorial Sloan-Kettering Cancer Center between 1982 and 1989. Metastasis free survival was the end point of the study. The prognostic value of the four staging systems and their components were examined in univariate and multivariate analyses. The Akaike information criterion (AIC) was used to identify the system that best predicted the risk of systemic recurrence. RESULTS: Compartment status, depth, grade, and size were all independent predictors of outcome within their respective staging systems. However, when compared with one another, only depth, grade, and size retained their prognostic significance. Of the four models, the AIC predicted that the MSK was the best predictor of systemic relapse, followed by the fifth edition of the AJCC/UICC staging system. CONCLUSIONS: Staging systems such as the MSK system or the fifth edition of the AJCC/UICC system, which include tumor depth, grade, and size as prognostic factors, are the most predictive of systemic relapse in patients presenting with localized extremity STS. Both of these systems identify the same group of patients at the highest risk who would be the most suitable for adjuvant chemotherapy trials.
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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) | 0.001 | 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".