Dataset for the Reporting of Merkel Cell Carcinoma
Bibliographic record
Abstract
Accurate and complete pathology reports are critical for the optimal management of cancer patients. Protocols for the pathologic reporting of Merkel cell carcinoma (MCC) have been developed independently by the Royal College of Pathologists (UK) and the College of American Pathologists. In this study, data elements for pathologic reporting of MCC were analyzed by an international panel of pathologists and clinicians with the aim of developing a common, internationally agreed upon dataset useful for clinical practice. The International Collaboration on Cancer Reporting expert review panel developed a protocol containing "core" (required) and "noncore" (recommended) elements. Core elements were defined as those that had evidentiary support and were unanimously agreed upon by the review panel as essential for the clinical management, staging, and/or assessment of prognosis in patients with MCC. Noncore elements were those considered to be clinical of interest, but with lesser degrees of supportive evidence or nonactionable implications. Ten core data elements for pathology reports on primary MCC were defined. Development and agreement on this evidence-based protocol at an international level was accomplished in a timely and efficient manner. The template developed for melanoma reporting was used as a structural base for this initiative. It is applicable to, and may facilitate the development of, protocols for other tumor types. Widespread utilization of an internationally agreed upon structured pathology dataset for MCC can be expected to lead to improved patient management. It should also facilitate collaborative clinical research.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.015 |
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".