Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the updated literature on middle ear adenomatous neuroendocrine tumors (MEANTS) and to discuss advances in classification, diagnosis, and management of these tumors. RECENT FINDINGS: The WHO updated its classification of head and neck neuroendocrine neoplasms in 2022. We discuss this classification system, and its implications on the diagnosis of these tumors from a histological and molecular perspective. Furthermore, this framework helps with our understanding of their clinical course and hence management. SUMMARY: In 2022, WHO classified head and neck neuroendocrine neoplasms into well differentiated neuroendocrine tumors (NET) (G1-G3, based on mitotic count/Ki67) and high-grade neuroendocrine carcinoma (NEC) (small/large cell), based on differentiation, atypia, and marker expression. Aside from histological characteristics, the WHO classification distinguishes NETs (site-specific epigenetic changes) from NECs (TP53/RB1 alterations). Small cell NECs show biallelic TP53/RB1 inactivation; large cell NECs are heterogeneous. Molecular profiling helps differentiate NET G3 from NEC. Recent reviews have shown higher rates of recurrence than previous studies, emphasizing the need for surgical modification based on tumor extent and biology, and for indefinite surveillance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.007 | 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".