Large cell neuroendocrine carcinoma of the ovary: A pathologic entity in search of clinical identity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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 it