The changing distribution of pT stage in testicular germ cell tumours from 1976 to 2005: a single‐centre analysis of histopathological reports
Bibliographic record
Abstract
As often happens, the huge interest in prostate cancer is reflected in the many papers which appear in press, and this is the case in this section, with other papers on renal and testicular cancer. However, the papers come from a wide variety of countries from all over the world: Austria, USA, Japan, UK, Italy, Germany and Canada. It is very pleasing to see the international motif of the Journal being maintained. OBJECTIVE To assess the changing distribution of stage in seminoma and nonseminomatous germ cell tumours (NSGCTs), as recent reports contained no detailed information on pT stage and vascular invasion, important factors in the decision for further treatment. PATIENTS AND METHODS Histopathological reports from 1976 to 2005, from patients who had surgery for testicular tumours at the authors’ institution, were investigated with special focus on pT stage and its distribution. The whole study period was divided into six 5‐year periods. The incidence of seminoma was compared with NSGCT, defined according to the Tumour‐Nodes‐Metastasis (TNM) classification. RESULTS In each 5‐year period the median number of tumours treated surgically was 86; the distribution of seminomas and NSGCTs remained stable during the study period ( P = 0.201). pT4 and pT3 tumours declined or disappeared in both histopathological groups, while pT2 and pT1 tumours increased during the study period. Since 1996–2000, pT1 tumours decreased, whereas pT2 tumours increased in seminomas ( P = 0.085) and NSGCTs ( P = 0.003). CONCLUSION The incidence of seminoma and NSGCT has not changed over the last 30 years in Vienna. With the establishment of vascular invasion in the TNM classification in 1997, the incidence of pT1 decreased while that of pT2 increased. Since then, the incidence of pT1 tumours in seminomas was 45.8% and 29.1% in NSGCT. According to generally accepted treatment guidelines, these patients might need no adjuvant treatment after surgery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| 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".