Dataset for reporting of prostate carcinoma in radical prostatectomy specimens: recommendations from the International Collaboration on Cancer Reporting
Bibliographic record
Abstract
This project was designed to harmonise the Royal College of Pathologists, College of American Pathologists and Royal College of Pathologists of Australasia datasets, checklists and structured reporting protocols for examination of radical prostatectomy specimens, with the aim of producing a common, internationally agreed, evidence-based dataset for prostate cancer reporting. The International Collaboration on Cancer Reporting prostate cancer expert review panel analysed the three existing datasets, identifying concordant items and classified these data elements as 'required' (mandatory) or 'recommended' (non-mandatory), on the basis of the published literature up to August 2011. Required elements were defined as those that have agreed evidentiary support at NHMRC level III-2 or above. Consensus response values were formulated for each item. Twelve concordant pathology data elements were identified, and, on review, all but one were included as required elements for tumour staging, grading, or prediction of prognosis. There was minor discordance between the three existing datasets for another eight items, with two of these being added to the required data set. Another 11 elements with a lesser level of evidentiary support were included in the recommended dataset. This process was found to be an efficient method for producing an evidence-based dataset for prostate cancer. Such internationally agreed datasets should facilitate meaningful comparison of benchmarking data, epidemiological studies, and clinical trials.
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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 | 0.291 | 0.316 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.022 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.016 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".