Local recurrence of localized soft tissue sarcoma
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to examine the effect of known predictors of local recurrence of soft tissue sarcoma in a competing risk setting. METHODS: The outcome of interest was the cumulative probability of local recurrence per category of relevant predictors, with death as a competing event. In total, 1668 patients with a localized soft tissue sarcoma of the extremity or trunk were included. RESULTS: Tumor size (hazard ratio, 3.3), depth (hazard ratio, 3.2), and histologic grade (hazard ratio, 4.5) were the variables that had the most effect on the risk of metastasis and, accordingly, were the most likely to induce competition. Surgical margins (hazard ratio, 3.3), histologic grade (hazard ratio, 2.1), presentation status (hazard ratio, 2.4), and tumor depth (hazard ratio, 1.5) were the variables that had the most effect on the risk of local recurrence. The 10-year cumulative probabilities of local recurrence were markedly different within categories for presentation status (P < .001) and surgical margin status (P < .001). However, because of the competing effect of death, there was little difference in the 10-year cumulative probabilities of local recurrence with regard to tumor depth (12% and 11.4% for deep and superficial tumors, respectively; P = .2), tumor size (10.6% and 13.3% for large and small tumors, respectively; P = .99), or histologic tumor grade (12.6%, 10.7%, and 11.1% for high, intermediate, and low-grade tumors, respectively; P = .17). CONCLUSIONS: Because of the competition between local recurrence and death, histologic tumor grade, tumor size, and tumor depth had little influence on the cumulative probability of local recurrence. The authors concluded that local management should be based on presentation status and surgical margins rather than other, previously acknowledged factors.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".