Long‐term oncological outcomes of patients with paratesticular sarcoma
Bibliographic record
Abstract
OBJECTIVES: To present long-term oncological outcomes of patients with paratesticular sarcoma treated by a multidisciplinary team. PATIENTS AND METHODS: Patients managed at the Princess Margaret Cancer Centre, between 1990 and 2012, were analysed. A sarcoma expert performed central pathology review. Kaplan-Meier graphs compared local recurrence (LR), metastasis, and overall survival (OS) of patients treated with hemiscrotectomy vs those who did not. Univariable Cox proportional hazards analysis was performed to delineate predictors of LR, metastasis, and OS. RESULTS: Overall, 51 patients with a median (interquartile range) follow-up of 132 (51.6-226.8) months were analysed. At presentation, 92.2% (47 patients) had localised disease. Only five patients (9.8%) had undergone initially planned hemiscrotectomy. Completion and salvage hemiscrotectomy was performed in 25 (54.3%) and seven (15.2%) patients, respectively. Recurrence and metastasis occurred in 12 (25.5%) and 10 patients (19.6%), respectively. At the last follow-up, 21.6% (11 patients) had died, with eight dying from their disease. Kaplan-Meyer graphs demonstrated that hemiscrotectomy improved LR (median not reached vs 62.4 months, log-rank P = 0.008) and OS (median not reached vs 168 months, log-rank P = 0.081). Univariable analysis found hemiscrotectomy to be associated with a lower LR rate (hazard ratio [HR] 0.21, P = 0.02), whilst positive margins at initial surgery were associated with increased LR (HR 4.81, P = 0.047). No metastasis predictors were found, but age (HR 1.04, 95% confidence interval [CI] 1.0-1.08; P = 0.02) and non-localised disease at presentation (HR5.17, 95% CI 1.33-20.06; P = 0.017) were associated with worse OS. CONCLUSION: Paratesticular sarcoma is a rare tumour, predominantly manifesting as localised disease. Most patients receive an initial suboptimal oncological surgery. Improved long-term outcomes are demonstrated following early hemiscrotectomy.
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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 | 0.000 | 0.002 |
| 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 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".