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TRV-2026-0860Certified recordPeer-reviewed

Combining Statistical Modeling and Machine Learning for Prognostic Feature Selection in Cervical Cancer: A Retrospective Study Based on SEER 2004 to 2015 Data

Objectives Cervical cancer is a leading female malignancy with high global morbidity/mortality, and remains high recurrence risk after standard treatment. Accurate prognostic feature identification is critical for personalized therapy and patient survival improvement, while traditional indicators and single biomarkers lack sufficient accuracy in prognostic prediction. Methods In this population-based retrospective study, we analyzed 5392 patients with cervical squamous cell carcinoma from the surveillance, epide…

Health · G Space — documented gain · certified 2026-08-24 · v1 · article view · machine-readable

Current reading — gain

A model combining clinical and sociodemographic variables predicted overall survival in cervical squamous cell carcinoma with acceptable discrimination.

What this doesn’t fix

Findings are limited to a retrospective cohort of cervical squamous cell carcinoma from SEER 2004-2015 and may not generalize beyond that population and period.

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Truvace Impact Record TRV-2026-0860, v1: “Combining Statistical Modeling and Machine Learning for Prognostic Feature Selection in Cervical Cancer: A Retrospective Study Based on SEER 2004 to 2015 Data.” Truvace, 2026-08-24. /record/TRV-2026-0860 (accessed at citation time). sha256 47cc71df0d783b26…

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