A contemporary population-based study of testicular sex cord stromal tumours: Presentation, treatment patterns, and predictors of outcome
Bibliographic record
Abstract
INTRODUCTION: We aimed to characterize demographic distribution, patient outcomes, and prognostic features of testicular sex cord stromal tumours (SCST) using a large statewide database. METHODS: Adult male patients diagnosed with SCST between 1988 and 2010 were identified within the California Cancer Registry (CCR). Baseline demographic variables and disease characteristics were reported. Primary outcome measures were cancer-specific survival (CSS) and overall survival (OS). Bivariate and multivariate Cox proportional hazards models were employed to identify predictors of survival. RESULTS: A total of 67 patients with SCST were identified, of which 45 (67%) had Leydig cell and 19 (28%) had Sertoli cell tumours. Median age was 40 years and the majority of patients (84%) presented with localized disease. Following orchiectomy, nine patients (15%) underwent retroperitoneal lymph node dissection (RPLND), whereas 54 patients (80%) had no further treatment. With a median followup of 75 months, two-year OS and CSS was 91% and 95%, respectively, for those presenting with stage I disease. For those presenting with stage II disease, two-year OS and CSS was 30%. Predictors of worse OS included age >60 (hazard ratio [HR] 5.64; p<0.01) and metastatic disease (HR 8.56; p<0.01). Presentation with metastatic disease was the only variable associated with worse CSS (HR 13.36; p<0.01). Histology was not found to be a significant predictor of either CSS or OS. CONCLUSIONS: We present the largest reported series to date for this rare tumour and provide contemporary epidemiological and treatment data. The primary driver of prognosis in patients with SCST is disease stage, emphasizing the importance of early detection and intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".