Accuracy of germ cell tumor histology and stage within a Canadian cancer registry
Bibliographic record
Abstract
INTRODUCTION: Cancer registries are the mainstay for Canadian population-based cancer statistics. Data are collected in provincial and territorial registries, including the Nova Scotia Cancer Registry (NS CR). The goal of this study was to determine the accuracy of NS CR data for germ cell tumors (GCT). METHODS: This analysis included all NS CR patients diagnosed with GCT from 2006-2015. The date and method of diagnosis, primary site, histology, and stage were recorded from the NS CR and compared to each patient's chart. Any discrepancies between the two sources were reviewed and reasons behind the discrepancies recorded. RESULTS: A total of 229 patients made up the study cohort. Using NS CR data, 57.6% had seminoma, 34.5% non-seminoma (NSG CT), and 7.9% other. Discrepancies in pathology were noted in 16 patients (7.0%). Using NS CR staging data (available in 185 cases), 71.9% had stage I, 12.4% stage II, 11.9% stage III, and 3.8% other. Discrepancies in stage were noted in 32 patients (17.3%) with NS CR data downstaging eight patients (4.3%) and upstaging 21 patients (11.4%). The site of the primary GCT was discrepant in 12 patients (5.2%). The date of diagnosis was accurate within one week for all patients except one. CONCLUSIONS: Higher-level NS CR data, such as date of diagnosis and overall pathological diagnosis, appear relatively accurate; however, there are inaccuracies in histological subtype and stage. This study raises awareness of these gaps and highlights key areas for improvement in educating registry personnel who interpret and enter data about the uniqueness of GCT pathology, staging, and interpretation of tumor markers.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".