Accuracy of germ cell tumor histological subtype and stage within the Canadian cancer registry.
Bibliographic record
Abstract
411 Background: Cancer registries are the mainstay for Canadian population-based cancer statistics and research. Data is collected in provincial and territorial registries including the Nova Scotia Cancer Registry (NSCR). The goal of this study was to determine the accuracy of the NSCR data regarding the diagnosis, primary site, histological subtype, and stage of germ cell tumors (GCT) when compared to the individual pathology reports and staging investigations from the chart. Methods: This analysis included all NSCR patients diagnosed with GCT from 2006-2015. From the NSCR, the date and method of diagnosis, primary site, histology, and stage were recorded. This data was also extracted from each patient’s chart record. Any discrepancies between the two sources were reviewed and reasons behind the discrepancies were recorded. Results: 239 unique patients were identified in the NSCR during the specified time period. Ten were excluded as no chart records were available to confirm accuracy. 229 patients make up the study cohort. Using NSCR data 57.6% had seminoma, 34.5% nonseminoma (NSGCT), and 7.9% other. Discrepancies in pathology were noted in 29 patients (12.7%) for a number of reasons including no appropriate coding option in the NSCR, multiple tumors, biopsy only specimens with misinterpretation of tumor marker elevation, and true coding error. Using NSCR 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 33 patients (17.9%) with NSCR data downstaging 10 patients (5.4%) and upstaging 19 patients (10.3%) predominantly due to miscoding patients as stage IS. The site of the primary GCT was discrepant in 12 patients (5.2%) due primarily to difficulty coding post chemotherapy orchiectomy specimens and burnt out primary testicular lesions. The date of diagnosis was accurate within 1 week for all patients except one which differed by several months. Conclusions: Canadian cancer registry data is population based and used for many purposes including policy decisions and research. The NSCR higher level data such as date of diagnosis and overall pathological diagnosis appears relatively accurate. However, there are inaccuracies in more detailed information like histological subtype and stage. This study raises awareness of these gaps. It also 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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.031 |
| 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 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".