Prechemotherapy Not Preorchiectomy Serum Tumor Markers Accurately Identify International Germ Cell Cancer Collaborative Group Prognostic Groups in Nonseminoma
Bibliographic record
Abstract
Levels of the serum tumor markers (STMs) α-fetoprotein, human chorionic gonadotropin, and lactate dehydrogenase are used in staging classification for metastatic germ-cell cancers and support decisions on the intensity of first-line treatment for patients with nonseminoma. Use of preorchiectomy instead of prechemotherapy STM levels can lead to inadequate classification. We identified 744 men with metastatic gonadal nonseminoma in the International Germ-Cell Cancer Collaborative Group (IGCCCG) Update Consortium database who had preorchiectomy and prechemotherapy STM levels available. Of these, 22% would have had inadequate IGCCCG prognostic group classification if preorchiectomy levels had been used, which would have resulted in overtreatment of 16% and undertreatment of 6% of men. These findings suggest that use of preorchiectomy instead of prechemotherapy STM results may lead to incorrect IGCCCG classification, which could compromise treatment success or expose patients to unnecessary toxicity. Patient summary: For men with testicular cancer, levels of tumor markers in their blood are used when making decisions on chemotherapy intensity. Use of test results for samples taken before removal of the cancer-bearing testicle instead of immediately before chemotherapy can lead to inadequate treatment recommendations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".