Pediatric Testicular Tumors: Contemporary Incidence and Efficacy of Testicular Preserving Surgery
Bibliographic record
Abstract
PURPOSE: Testicular tumors in the pediatric population are fundamentally distinct from their adult counterparts. We reviewed a contemporary single series from a large pediatric health science center. We also examined our experience with testis conserving surgery and then used it to develop a preoperative management algorithm. MATERIALS AND METHODS: A retrospective review was performed of all testicular tumors at a single institution from 1984 to 2002. Data were compiled using the American Academy of Pediatrics testis tumor registry data collection form. We further examined partial orchiectomies for indications and outcomes with respect to cancer control and testicular viability. RESULTS: A total of 51 primary testicular lesions were identified. Patient age was prenatal to 16 years with a scrotal mass the most common presentation (81%). Mature teratoma, rhabdomyosarcoma, epidermoid cyst, yolk sac and germ cell tumors accounted for 43%, 26%, 10%, 8% and 6% of cases, respectively. This distribution was markedly different from the last reported American Academy of Pediatrics data base. Organ preserving surgery was planned and achieved in 13 cases. All surgeries were successful with respect to cancer control and testicular preservation. CONCLUSIONS: We believe that the higher incidence of teratoma is more representative of this population and yolk sac tumor is a minority diagnosis. The single institution review eliminates the interinstitutional referral heterogeneity that may have skewed larger data bases. Furthermore, the concept of testicular preserving surgery becomes an attractive option since we present its safety and efficacy. The management algorithm should facilitate the preoperative decision to perform less radical surgery and help preserve testicular tissue.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".