Clinicopathologic predictors of outcomes in children with stage I germ cell tumors: A pooled <i>post hoc</i> analysis of trials from the Children’s Oncology Group.
Bibliographic record
Abstract
418 Background: Patients with clinical stage I (CS I: cN0M0) germ cell tumors (GCT) exhibit favorable oncologic outcomes. While prognostic features can help inform treatment in adults with CS I GCT, we lack reliable means to predict relapse among pediatric patients. We sought to identify predictors of relapse in children with CS I GCT. Methods: We performed a pooled post hoc analysis on pediatric CS I GCT patients enrolled in 3 prospective trials: INT-0097 (phase II), INT-0106 (phase III), and AGCT0132 (phase III). Pathology was centrally reviewed. Patient demographics, pT stage, serum tumor markers, margin status, histology, relapse, and survival were compiled. Cox regression analyses were used to identify predictors of outcomes. Results: 88 patients were identified with histologic data available. Most patients were pT1-2 stage. Yolk sac tumor was present in 75%, while 16% had embryonal carcinoma, and 9% had choriocarcinoma. When evaluable, lymphovascular invasion (LVI) was present in 36/66 (55%) of patients. Over a median follow-up of 5.0 years, no patients died and 24 patients (27%) relapsed (median relapse-free survival not reached). Predictors of relapse included presence of choriocarcinoma (HR 4.3, p=0.004), embryonal carcinoma (HR 3.8, p=0.002), pT3 stage (HR 6.9, p=0.027), and age >12 years (HR 3.1, p=0.011). LVI (HR 2.4, p=0.072), serum tumor markers, and dominant tumor size did not reach significance. Pediatric CS I GCT patients exhibit remarkable 5-year survival. Conclusions: Using combined data from multiple prospective trials, our study identifies clinicopathologic features that predict relapse and potentially inform personalized treatment for these patients.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".