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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".