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Variability in Predictions from Online Tools: A Demonstration Using Internet-Based Melanoma Predictors.

Emily C Zabor1, Daniel Coit2, Jeffrey E Gershenwald3

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Online prognostic tools show significant survival prediction variability, especially for lower predictions. Careful development and interpretation are crucial for these tools to aid patients and clinicians in understanding prognosis.

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Area of Science:

  • Medical Informatics
  • Oncology
  • Biostatistics

Background:

  • Online prognostic models are increasingly accessible to patients and clinicians.
  • These tools aid in understanding prognosis and informing treatment decisions.
  • Variability across online prognostic tools for a single disease warrants investigation.

Purpose of the Study:

  • To demonstrate variability in survival predictions across multiple online prognostic tools.
  • To assess the performance of these tools using a consistent validation dataset.

Main Methods:

  • Retrospective collection of melanoma patient data (2000-2014) from Memorial Sloan Kettering Cancer Center.
  • Application of a single validation dataset to three distinct online melanoma prognostic tools.
  • Assessment of model calibration using calibration plots and discrimination using the C-index.

Main Results:

  • Significant differences were observed across the three prognostic models.
  • Variability in individual patient survival predictions was noted, particularly in lower prediction ranges.
  • Calibration and discrimination metrics varied among the evaluated online tools.

Conclusions:

  • The study highlights potential variability within and across online prognostic tools.
  • Methodological rigor is essential for developing reliable publicly available prognostic models.
  • Careful development, interpretation, and understanding of limitations are vital for the utility of online survival prediction tools.