Neuroendocrine Carcinomas of the Uterine Cervix, Endometrium, and Ovary Show Higher Tendencies for Bone, Brain, and Liver Organotrophic Metastases
Bibliographic record
Abstract
Neuroendocrine carcinoma (NEC) of the female genital tract is a rare and aggressive subtype of cancer that is still poorly understood. Several recent studies reported that pulmonary and gastroenteropancreatic neuroendocrine neoplasms show significantly different patterns of metastasis compared to non-NECs of the same primary sites. The aim of this study was to evaluate the metastatic patterns of gynecologic NECs and to compare the metastatic patterns of NECs and non-NECs of the same primary sites. We retrieved and analyzed cervical, endometrial, and ovarian NEC cases from the Surveillance, Epidemiology, and End Results (SEER) database. To validate the results, we also retrieved and analyzed cervical NEC cases from an institutional database. Uterine cervical NEC was the most common NEC. The overall metastatic rate was significantly higher in the NEC group than in the non-NEC group for all three primary sites. All cervical, endometrial, and ovarian NECs showed a higher tendency for bone, brain, and liver organotrophic metastasis than non-NECs of the same primary sites. We demonstrated that gynecologic NECs show significantly different metastatic patterns compared to non-NECs of the same primary sites. These findings might help clinicians to better manage patients with gynecologic NECs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".