Large cell neuroendocrine carcinoma of the ovary: A pathologic entity in search of clinical identity
Bibliographic record
Abstract
Large cell neuroendocrine carcinoma (LCNEC) of the ovary is a rare diagnosis and only a few dozen cases have been reported in the literature. It is characterized by large pleiomorphic cells with large round or oval nuclei, presence of mitoses and staining for neuroendocrine (NE) markers such as chromogranin A, synaptophysin, neuron specific enolase. This editorial gives a brief overview of this histologic type of ovarian carcinomas. LCNEC of the ovary is a pathologic entity that may not be diagnosed purely on clinical grounds due to the similarity of its clinical features with those of the more common epithelial ovarian cancers. Nevertheless the diagnosis is worth-making from a practical point of view in order to consider treatments tailored towards the NE component if it is dominant or it becomes dominant during the natural evolution of the disease. Establishment of an international tumor registry with an accompanying tumor tissue bank of ovarian LCNEC could be a means of obtaining further knowledge on clinical characteristics and advance research on this rare entity. This will further inform on treatment strategies and could identify future molecular treatment targets.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".