An Illustrated Consensus on the Classification of Pancreatic Intraepithelial Neoplasia and Intraductal Papillary Mucinous Neoplasms
Bibliographic record
Abstract
Invasive pancreatic ductal adenocarcinoma is an almost uniformly fatal disease. Several distinct noninvasive precursor lesions can give rise to invasive adenocarcinoma of the pancreas, and the prevention, detection, and treatment of these noninvasive lesions offers the potential to cure early pancreatic cancers. Noninvasive precursors of invasive ductal adenocarcinoma of the pancreas include pancreatic intraepithelial neoplasias (PanINs), intraductal papillary mucinous neoplasms (IPMNs), and mucinous cystic neoplasms. Diagnostic criteria, including a distinct ovarian-type stroma, and a consistent nomenclature are well established for mucinous cystic neoplasms. By contrast, consistent nomenclatures and diagnostic criteria have been more difficult to establish for PanINs and IPMNs. Because both PanINs and IPMNs consist of intraductal neoplastic proliferations of columnar, mucin-containing cells with a variable degree of papilla formation, the distinction between these two classes of precursor lesions remains problematic. Thus, considerable ambiguities still exist in the classification of noninvasive neoplasms in the pancreatic ducts. A meeting of international experts on precursor lesions of pancreatic cancer was held at The Johns Hopkins Hospital from August 18 to 19, 2003. The purpose of this meeting was to define an international acceptable set of diagnostic criteria for PanINs and IPMNs and to address a number of ambiguities that exist in the previously reported classification systems for these neoplasms. We present a consensus classification of the precursor lesions in the pancreatic ducts, PanINs and IPMNs.
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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 | 0.043 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".