Pan-Canadian Analysis of Practice Patterns in Small Cell Carcinoma of the Cervix: Insights from a Multidisciplinary Survey
Bibliographic record
Abstract
Small-cell neuroendocrine carcinoma of the cervix (SCNECC) is a rare cancer with poor prognosis, with limited data to guide its treatment. The objective of this study was to evaluate practice patterns in the management of SCNECC. A 23-question online survey on management of SCNECC was disseminated to Canadian gynecologic oncologists (GO), radiation oncologists (RO) and medical oncologists (MO). In total, 34 practitioners from eight provinces responded, including 17 GO, 13 RO and four MO. During staging and diagnosis, 74% of respondents used a trimodality imaging approach, and 85% tested for neuroendocrine markers. In early-stage (1A1-1B2) SCNECC, 87% of practitioners used a surgical-based approach with various adjuvant and neoadjuvant treatments. In locally advanced (1B3-IVA) SCNECC, 53% favored primary chemoradiation, with cisplatin and etoposide, with the remainder using surgical or radiation-based approaches. In metastatic and recurrent SCNECC, the most common first-line regimen was etoposide and platinum, and 63% of practitioners considered clinical trials in the first line setting or beyond. This survey highlights diverse practice patterns in the treatment of SCNECC. Interdisciplinary input is crucial to individualizing multimodality treatment, and there is a need for prospective trials and intergroup collaboration to define the optimal approach towards managing this rare cancer type.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".