Adverse Effect of Older Age on the Recurrence of Soft Tissue Sarcoma of the Extremities and Trunk
Bibliographic record
Abstract
PURPOSE: To examine the effect of age on the recurrence of soft tissue sarcoma in the extremities and trunk. PATIENTS AND METHODS: This was a multicenter study that included 2,385 patients with median age at surgery of 57 years. The end points considered were local recurrence and metastasis. Cox proportional hazards models were used to estimate hazard ratios across the age ranges with and without adjustment for known confounding factors. RESULTS: Older patients presented with tumors that were larger (P < .001) and of higher grade (P < .001). The proportion of positive margins increased significantly as patients age (P < .001), but radiation therapy was relatively underused in patients older than age 60 years. The 5-year cumulative incidences of local recurrence were 7.2% (95% CI, 4% to 11.7%) for patients age 30 years or younger and 12.9% (95% CI, 9.1% to 17.5%) for patients age 75 years or older. The corresponding 5-year cumulative incidences of metastasis were 17.5% (95% CI, 12.1% to 23.7%) and 33.9% (95% CI, 28.1% to 39.8%) for the same groups. Regression models showed that age was significantly associated with local recurrence (P < .001) and metastasis (P < .001) in nonadjusted models. After adjusting for imbalance in presentation and treatment variables, age remained significantly associated with local recurrence (P = .031) and metastasis (P = .019). CONCLUSION: Older patients have worse outcomes because they tend to present with worse tumors and are treated less aggressively. However, there remained a significant increase in the risk of both local and systemic recurrence associated with increasing age that could not be explained by tumor or treatment characteristics.
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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) | 0.000 | 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 | 0.000 |
| 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 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".