Population-Based Survival Analysis of Stage IVB Small-Cell Neuroendocrine Carcinoma in Comparison to Major Histological Subtypes of Cervical Cancer
Bibliographic record
Abstract
The aim of the current study is to investigate the survival outcome of stage IVB SCNEC of the uterine cervix in comparison to major histological subtypes of cervical cancer. A population-based retrospective cohort study was conducted using the Osaka Cancer Registry data from 1994 to 2018. All FIGO 2009 stage IVB cervical cancer patients who displayed squamous cell carcinoma (SCC), adenocarcinoma (A), adenosquamous cell carcinoma (AS), or small-cell neuroendocrine carcinoma (SCNEC) were first identified. The patients were classified into groups according to the types of primary treatment. Then, their survival rates were examined using the Kaplan-Meier method. Overall, in a total of 1158 patients, clearly differential survival rates were observed according to the histological subtypes, and SCNEC was associated with shortest survival. When examined according to the types of primary treatments, SCNEC was associated with significantly decreased survival when compared to SCC or A/AS, except for those treated with surgery. In patients with FIGO 2009 stage IVB cervical cancer, SCNEC was associated with decreased survival when compared to SCC or A/AS. Although current treatments with either surgery, chemotherapy or radiotherapy have some therapeutic efficacies, to improve the prognosis, novel effective treatments specifically targeting cervical SCNEC need to be developed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".