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Application of clinical prediction modeling in pediatric neurosurgery: a case study.

Hendrik-Jan MijderwijkThomas BeezDaniel HänggiDaan Nieboer
Published in: Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery (2021)
There has been an increasing interest in articles reporting on clinical prediction models in pediatric neurosurgery. Clinical prediction models are mathematical equations that combine patient-related risk factors for the estimation of an individual's risk of an outcome. If used sensibly, these evidence-based tools may help pediatric neurosurgeons in medical decision-making processes. Furthermore, they may help to communicate anticipated future events of diseases to children and their parents and facilitate shared decision-making accordingly. A basic understanding of this methodology is incumbent when developing or applying a prediction model. This paper addresses this methodology tailored to pediatric neurosurgery. For illustration, we use original pediatric data from our institution to illustrate this methodology with a case study. The developed model is however not externally validated, and clinical impact has not been assessed; therefore, the model cannot be recommended for clinical use in its current form.
Keyphrases
  • healthcare
  • decision making
  • young adults
  • emergency department
  • machine learning
  • electronic health record
  • artificial intelligence
  • current status
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