Prognostic Significance of Complete Blood Count-Derived Inflammatory Biomarkers in Patients with Small Cell Neuroendocrine Carcinoma of the Cervix
Bibliographic record
Abstract
BACKGROUND: Small cell neuroendocrine carcinoma of the cervix (SCNEC) is a rare and highly aggressive malignancy with limited prognostic biomarkers available for clinical use. Inflammatory markers derived from complete blood count (CBC) have been shown to reflect the systemic immune response and tumor progression in various cancers, but their prognostic value in SCNEC remains unclear. METHODS: We retrospectively analyzed clinical data from patients diagnosed with SCNEC between 2004 and 2024 across two centers. Internal validation was performed by dividing patients into training and test cohorts. Cox regression analyses and Kaplan-Meier survival analyses were used to evaluate prognostic factors and treatment outcomes. Inverse probability of treatment weighting (IPTW) was applied to reduce baseline imbalances. Patients were randomly divided into training and test cohorts. A nomogram was constructed to predict 3-year and 5-year progression-free survival (PFS) with performance evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). RESULTS: = 0.029). The constructed nomogram demonstrated excellent predictive performance, with area under the curve (AUC) values of 0.799 and 0.787 for 3-year and 5-year PFS in the training cohort, and 0.802 for endpoints in the test cohort. CONCLUSIONS: MLR was identified as an independent prognostic biomarker for PFS in SCNEC, with potential value in risk stratification and personalized treatment strategies. Additionally, we developed a reliable nomogram that accurately predicts 3-year and 5-year PFS, serving as a practical tool for individualized prognosis and clinical decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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) | 0.001 | 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".