Primary Neuroendocrine Carcinoma of the Uterine Cervix Treated With Complete Surgical Resection and Adjuvant Combination Chemotherapy
Bibliographic record
Abstract
We describe our experience with one case of cervical large cell neuroendocrine carcinoma (LCNC), with an attempt of an adjuvant chemotherapy after complete surgery. The patient was a 66-year-old female (gravida 3, para 2) presenting with genital bleeding. A cervical mass was diagnosed as high-grade neuroendocrine carcinoma. Radical hysterectomy, pelvic lymph node dissection and bilateral salpingo-oophorectomy were performed as primary treatment for the cervical cancer. The surgical specimen of the uterus had an enlarged cervix of 4 cm in diameter with parametrial invasion. Microscopically, the surgical specimen exhibited invasive proliferation of relatively large tumor cells. Peripheral nuclear palisading and central necrosis were also histologically observed. Tumor cells had abundant cytoplasm with vesicular nuclei. The final pathological conclusion was high-grade neuroendocrine carcinoma (LCNC). The postoperative diagnosis was cervical cancer, high-grade neuroendocrine carcinoma (LCNC), pT2bN0M0, FIGO stage IIB, ly (+), v (+). Adjuvant treatment with cisplatin and irinotecan was planned at 4-week intervals. With the completion of three cycles of adjuvant chemotherapy, there was no evidence of recurrent disease. We determined the adjuvant therapy to be effective and well tolerated, and the therapy is now planned to be continued. In conclusion, we experienced a rare case of uterine cervical LCNC. It was surgically resected completely, and adjuvant chemotherapy has been continued. Adjuvant combination chemotherapy with cisplatin and irinotecan is expected to improve the prognosis of cervical LCNC. J Clin Gynecol Obstet. 2017;6(1):23-27 doi: https://doi.org/10.14740/jcgo432w
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".