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Related Experiment Video

Updated: Jun 24, 2026

An Experimental Paradigm for the Prediction of Post-Operative Pain (PPOP)
14:56

An Experimental Paradigm for the Prediction of Post-Operative Pain (PPOP)

Published on: January 27, 2010

Perioperative Symptom Trajectories and a Risk Prediction Model for Cervical Cancer: A Prospective Longitudinal Study.

Yuting Zhang1, Yaru Wu1, Yuting Wang1

  • 1School of Nursing, Xinjiang Medical University, Urumqi, Xinjiang, People's Republic of China.

International Journal of Women'S Health
|June 23, 2026
PubMed
Summary

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This summary is machine-generated.

This study identified five symptom clusters and three recovery trajectories in cervical cancer patients. A predictive model using age, operative factors, and hematological indicators accurately identifies high-risk patients for improved perioperative care.

Area of Science:

  • Oncology
  • Symptom Science
  • Clinical Prediction Modeling

Background:

  • Cervical cancer patients experience complex perioperative symptoms.
  • Understanding symptom clusters and trajectories is crucial for effective management.
  • Identifying risk factors aids in proactive patient care.

Purpose of the Study:

  • To characterize perioperative symptom clusters in cervical cancer patients.
  • To identify factors influencing symptom trajectories.
  • To develop a clinical risk prediction model for symptom distress.

Main Methods:

  • Prospective longitudinal study with symptom assessment (MDASI-PeriOp-GYN).
  • Exploratory factor analysis and linear mixed models for cluster and trajectory analysis.
Keywords:
cervical cancerhematological indicatorslinear mixed modellongitudinal studyperioperative periodrisk prediction modelsymptom clusters

Related Experiment Videos

Last Updated: Jun 24, 2026

An Experimental Paradigm for the Prediction of Post-Operative Pain (PPOP)
14:56

An Experimental Paradigm for the Prediction of Post-Operative Pain (PPOP)

Published on: January 27, 2010

  • Logistic regression and restricted cubic splines for predictor screening; model validation via ROC and calibration curves.

    • Main Results:

    • Five distinct symptom clusters were identified: somatic, psycho-neurological, gastrointestinal/energy, emotional/eating, and consciousness/sedation.
    • Three patient trajectories emerged: well-recovered, moderately persistent, and severely distressed.
    • Key predictors included age, operative duration, fibrinogen, neutrophil-to-lymphocyte ratio, D-dimer (risk factors), and serum albumin, serum potassium (protective factors).
    • A seven-indicator model achieved an AUC of 0.863, demonstrating superior predictive performance.

    Conclusions:

    • Patients in the severe distress trajectory exhibit rapid symptom changes near discharge, indicating a high-risk group.
    • A Nomogram prediction model effectively stratifies patients, enabling targeted interventions.
    • The model enhances perioperative symptom management and transitional care for cervical cancer patients.