Second edition <scp>ICCR</scp> dataset for testicular germ cell tumours: a reporting guide for histopathological diagnosis of orchiectomy specimens
Bibliographic record
Abstract
To summarise the content and significance of the recently published second edition International Collaboration on Cancer Reporting (ICCR) histopathology dataset for testicular germ cell tumours, covering the Orchiectomy specimen dataset. We highlight key updates from the first editions, including alignment with the 5th edition World Health Organization (WHO) Classification, revised staging criteria, clarified core data elements versus non-core elements and the evidentiary basis underpinning these changes. A review of the ICCR 2nd edition dataset for Orchiectomy specimens of primary testicular tumours was performed, focusing on their development by an international expert committee using a consensus-based approach. Core (required) and non-core (recommended) data elements were identified along with the level of evidence supporting each, following National Health and Medical Research Council (NHMRC) criteria. Changes from the first edition were extracted by comparing dataset content and notes, informed by up-to-date literature through July 2024. The 2nd edition Orchiectomy dataset provides an integrated, harmonised framework for reporting testicular germ cell tumours. The dataset incorporates the WHO 5th Edition Classification of Urinary and Male Genital Tumours. Pathological staging criteria have been updated to align with the 8th edition Union for International Cancer Control (UICC)/American Joint Committee on Cancer (AJCC) tumour-node-metastasis (TNM) definitions. The second edition of this dataset includes changes to align the dataset with the WHO Classification of Tumours, Urinary and Male Genital Tumours, 5th edition, 2022. The ICCR dataset includes the 5th edition Corrigenda, July 2024. It was agreed that this dataset is not suitable for non-germ cell tumours, with the hope that a new dataset, especially for sex-cord stromal tumours, would be developed. The 2nd edition Orchiectomy dataset represents an authoritative, up-to-date standard for pathology reporting of primary testicular germ cell tumours. By incorporating the WHO 5th edition classifications, current TNM staging and the latest evidence on prognostic factors, this dataset facilitates uniform reporting and prognostication. The ICCR dataset underscores core data required for patient management decisions (e.g., adjuvant therapy in Stage I disease, post-chemotherapy management) while providing flexibility through non-core elements for additional useful information. Adoption of this internationally vetted dataset will enhance consistency, assist multidisciplinary treatment planning and align pathology reports with modern consensus guidelines and classifications. The dataset can be used in both high-resource and limited-resource settings without compromising the essential reporting standards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.127 | 0.049 |
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".