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Related Experiment Video

Updated: Jan 7, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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The Necessity and Feasibility Assessment Tool of the Clinical Prediction Model for Individual Prognosis Before Its

Xiaohang Liu1, Yaguang Peng1, Nan Li2,3

  • 1Center for Clinical Epidemiology and Evidence-Based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, Beijing, China.

Journal of Evidence-Based Medicine
|December 30, 2025
PubMed
Summary

A new assessment tool, the FATCLIP, helps researchers evaluate the necessity and feasibility of clinical prediction models. This ensures efficient research and avoids wasted resources in developing these vital prognostic tools.

Keywords:
Delphi techniqueclinical prediction modelevidence‐based medicineprognosisquality improvement

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Area of Science:

  • Clinical Epidemiology
  • Biostatistics
  • Health Services Research

Background:

  • Many clinical prediction models remain unapplied, necessitating rigorous justification for new research.
  • An evidence-based review is crucial to validate the need for developing novel prediction models.

Purpose of the Study:

  • To develop an assessment tool for evaluating the necessity and feasibility of clinical prediction models.
  • To aid researchers and peer reviewers in rapid, comprehensive project evaluations.

Main Methods:

  • A steering group of 15 experts defined the scope, framework, and item pool.
  • An iterative Delphi process involving 34 multidisciplinary experts refined the assessment tool.
  • The framework development followed established quality assessment tool guidelines.

Main Results:

  • The Necessity and Feasibility Assessment Tool of CLInical Prediction models for individual prognosis (FATCLIP) was developed.
  • Expert consensus established a framework comprising 6 domains and 31 signaling questions.
  • Domains include prediction outcome, existing model review, predictors, data, development/validation, and application.

Conclusions:

  • FATCLIP assists in identifying potential challenges in clinical prediction model development and application.
  • The tool promotes efficient research and prevents resource waste in the field.
  • It supports informed decisions before initiating clinical prediction model projects.