Data Set for Reporting of Lung Carcinomas: Recommendations From International Collaboration on Cancer Reporting
Bibliographic record
Abstract
CONTEXT: The International Collaboration on Cancer Reporting (ICCR) is a quadripartite alliance formed by the Royal College of Pathologists of Australasia, the Royal College of Pathologists of the United Kingdom, the College of American Pathologists, and the Canadian Partnership Against Cancer. The ICCR was formed with a view to reducing the global burden of cancer data set development and reduplication of effort by different international institutions that commission, publish, and maintain standardized cancer-reporting data sets. The resultant standardization of cancer reporting would be expected to benefit not only those countries directly involved in the collaboration but also others not in a position to develop their own data sets. OBJECTIVES: To develop an evidence-based reporting data set for each cancer site. DESIGN: A project to develop data sets for prostate, endometrium, and lung cancers and malignant melanoma was piloted by the quadripartite group. RESULTS: A set of required and recommended data elements and appropriate responses for each element were agreed upon for the reporting of lung cancer. CONCLUSIONS: This review describes the process of development of the lung cancer data set.
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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 | 0.321 | 0.389 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.028 | 0.028 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.018 | 0.009 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".