Mixed neuroendocrine-non-neuroendocrine neoplasms of the digestive system: A mini-review
Bibliographic record
Abstract
Mixed neuroendocrine-non-neuroendocrine neoplasms (MiNENs) are rare mixed tumors containing both neuroendocrine (NE) and non-NE components. Each component must occupy at least 30% of the tumor volume by definition. Recent molecular evidence suggests MiNENs are clonal neoplasms and potentially harbor targetable mutations similar to conventional carcinomas. There have been multiple changes in the nomenclature and classification of MiNENs which has created some confusion among pathologists on how to integrate the contributions of each component in a MiNEN, an issue which in turn has resulted in confusion in communication with front-line treating oncologists. This mini review summarizes our current understanding of MiNENs and outline diagnosis, prognosis, and management of these neoplasms. The authors emphasize the importance of treating the most aggressive component of the tumor regardless of its percentage volume.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".