Regional Differences in Stage III Nonseminoma Germ Cell Tumor Patients Across SEER Registries
Bibliographic record
Abstract
PURPOSE: We investigated regional differences in patients with stage III nonseminoma germ cell tumor (NSGCT). Specifically, we investigated differences in baseline patient, tumor characteristics and treatment characteristics, as well as cancer-specific mortality (CSM) across different regions of the United States. METHODS: Using the Surveillance, Epidemiology, and End Results (SEER) database (2004-2018), patient (age, race/ethnicity), tumor (International Germ Cell Cancer Collaborative Group [IGCCCG] prognostic groups) and treatment (systemic therapy and retroperitoneal lymph dissection [RPLND] status) characteristics were tabulated for stage III NSGCT patients, according to 12 SEER registries representing different geographic regions. Multinomial regression models and multivariable Cox regression models testing for cancer-specific mortality (CSM) were used. RESULTS: In 3,174 stage III NSGCT patients, registry-specific patient counts ranged from 51 (1.5%) to 1630 (51.3%). Differences across registries existed for age (12%-31% for age 40+), race/ethnicity (5%-73% for others than non-Hispanic whites), IGCCCG prognostic groups (24%-43% vs. 14-24% vs. 3%-20%, in respectively poor vs. intermediate vs. good prognosis), systemic therapy (87%-96%) and RPLND status (12%-35%). After adjustment, clinically meaningful inter-registry differences remained for systemic therapy (84%-97%) and RPLND (11%-32%). Unadjusted 5-year CSM rates ranged from 7.1% to 23.3%. Finally in multivariable analyses addressing CSM, 2 registries exhibited more favorable outcomes than SEER registry of reference (SEER Registry 12): SEER Registry 4 (Hazard Ratio (HR): 0.36) and SEER Registry 9 (HR: 0.64; both P = .004). CONCLUSION: We identified important regional differences in patient, tumor and treatment characteristics, as well as CSM which may be indicative of regional differences in quality of care or expertise in stage III NGSCT management.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".