Survival of Testicular Pure Teratoma vs. Mixed Germ Cell Tumor Patients in Primary Tumor Specimens across All Stages
Bibliographic record
Abstract
We aimed to test for survival differences between testicular pure teratoma vs. mixed germ cell tumor (GCT) patients in a stage-specific fashion. Pure teratoma and mixed GCT in primary tumor specimens were identified within the Surveillance, Epidemiology, and End Results database (2004–2019). Kaplan–Meier curves depicted five-year overall survival (OS) and subsequently, cumulative incidence plots depicted cancer-specific mortality (CSM) and other-cause mortality (OCM) in a stage-specific fashion. Multivariable competing risks regression (CRR) models were used. Of 9049 patients, 299 (3%) had pure teratoma. In stage I, II and III, five-year OS rates differed between pure teratoma and mixed GCT (stage I: 91.6 vs. 97.2%, p < 0.001; stage II: 100 vs. 95.9%, p < 0.001; stage III: 66.8 vs. 77.8%, p = 0.021). In stage I, survival differences originated from higher OCM (6.4 vs. 1.2%; p < 0.001). Conversely in stage III, survival differences originated from higher CSM (29.4 vs. 19.0%; p = 0.03). In multivariable CRR models, pure teratoma was associated with higher OCM in stage I (Hazard Ratio (HR): 4.83; p < 0.01). Conversely, in stage III, in multivariable CRR models, pure teratoma was associated with higher CSM (HR: 1.92; p = 0.04). In pure teratoma, survival disadvantage in stage I patients relates to OCM. Survival disadvantage in stage III pure teratoma originates from higher CSM.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".