Clinical outcomes of neuroendocrine carcinoma of the cervix: Retrospective review from a large academic cancer centre
Bibliographic record
Abstract
Objective: Neuroendocrine cervical carcinomas are a rare but aggressive malignancy associated with a poor prognosis and there is limited evidence to guide clinical decision-making. Our objective was to evaluate the patterns of practice and clinical outcomes of patients diagnosed with neuroendocrine cervical carcinoma. Methods: This was a retrospective chart review of patients diagnosed with neuroendocrine cervical carcinoma between 2007 and 2023. Demographic, treatment, and outcome data were extracted from the medical records and summarized using descriptive statistics. Results: In total 32 patients were identified. Median follow-up was 14.5 months, and median age at diagnosis was 52 (range 21-89), 31.3 % (10/32) were stage IVB at time of diagnosis. Primary treatment consisted of surgery in 10 patients (31.3 %) and chemo-radiation in 15 patients (46.9 %), with the remainder of patients (7/32, 21.9 %) receiving upfront palliative therapy. Adjuvant chemotherapy typically consisted of a combination of cisplatin and etoposide. Median OS for the full cohort was 19 months (2-year OS 39 %, 2-year LR 9 %, 2-year LRR 9 %). Primary surgery was generally offered to patients with earlier stage disease (IA2-IIA1) relative to primary chemoradiotherapy (IB1-IVB). Patients treated with primary surgery had significantly higher median OS compared to those treated with primary chemoradiotherapy (39 vs 19 months, p = 0.04). Treatment failure usually consistent of distant metastatic relapse (15/20, 75 %). Conclusion: In our single institution review of neuroendocrine carcinoma of the cervix, primary surgery was associated with improved OS; however, our sample size was small with a bias to offering upfront surgery to patients with earlier stage disease.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".