Handling and reporting of orchidectomy specimens with testicular cancer: areas of consensus and variation among 25 experts and 225 European pathologists
Bibliographic record
Abstract
AIMS: The handling and reporting of testicular tumours is difficult due to their rarity. METHODS AND RESULTS: A survey developed by the European Network of Uro-Pathology (ENUP) and sent to its members and experts to assess the evaluation of testicular germ cell tumours. Twenty-five experts and 225 ENUP members replied. Areas of disagreement included immaturity in teratomas, reported by 32% of experts but 68% of ENUP. Although the presence of rete testis invasion was reported widely, the distinction between pagetoid and stromal invasion was made by 96% of experts but only 63% of ENUP. Immunohistochemistry was used in more than 50% of cases by 68% of ENUP and 12% of experts. Staging revealed the greatest areas of disagreement. Invasion of the tunica vaginalis without vascular invasion was interpreted as T1 by 52% of experts and 67% of ENUP, but T2 by the remainder. Tumour invading the hilar adipose tissue adjacent to the epididymis without vascular invasion was interpreted as T1: 40% of experts, 43% of ENUP; T2: 36% of experts, 30% of ENUP; and T3: 24% of experts, 27% of ENUP. CONCLUSIONS: There is remarkable consensus in many areas of testicular pathology. Significant areas of disagreement included staging and reporting of histological types, both of which have the potential to impact on therapy.
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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 | 0.040 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".