PREPUBERTAL TESTIS TUMORS: ACTUAL PREVALENCE RATE OF HISTOLOGICAL TYPES
Bibliographic record
Abstract
PURPOSE: Tumor registries, urological textbooks and literature surveys all assert that yolk sac tumors are the most common primary testicular tumors in boys 12 years and younger. In contrast, several individual institutions have reported that benign tumors are more common than malignant tumors. To clarify these discordant findings, we surveyed the primary pathology records from 4 major pediatric centers. MATERIALS AND METHODS: The pathology records of the contributing centers were culled for primary testicular masses in boys 12 years and younger. Older boys and those with either paratesticular tumors or leukemia were excluded. The prevalence of each histological subtype was calculated from the pooled cases. RESULTS: A total of 98 patients met our criteria. Only 15% had yolk sac tumors. Teratomas comprised 48% of the tumors (mature 44%, immature 4%). Epidermoid cysts were found in another 14% of patients. Gonadal stromal cell tumors represented 13% of the total, divided among granulosa cell (5%), Leydig cell (4%), Sertoli cell (3%) and mixed gonadal stromal cell (1%). Other pathology, including cystic dysplasia (2), lymphoma (4), inflammatory pseudotumor (1) and gonadoblastoma (2), made up 9% of the total number of cases. CONCLUSIONS: We found that benign lesions represent the majority of primary testis tumors (74%), with the most common histological type being teratoma (48%). The reported high prevalence rates of prepubertal yolk sac tumors probably results from a reporting bias, since benign tumors are less likely to be submitted to tumor registries. Therefore, the primary operative approach to the majority of testis tumors in boys 12 years and younger should entail testis sparing surgery. Orchiectomy should be reserved for histologically confirmed malignancy based on increased preoperative alpha-fetoprotein and/or frozen section analysis of the tumor.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".