Machine-learning-based models for the optimization of post-cervical spinal laminoplasty outpatient follow-up schedules.

ABSTRACT:

Background

Patients undergo regular clinical follow-up after laminoplasty for cervical myelopathy. However, those whose symptoms significantly improve and remain stable do not need to conform to a regular follow-up schedule. Based on the 1-year postoperative outcomes, we aimed to use a machine-learning (ML) algorithm to predict 2-year postoperative outcomes.

Methods

We enrolled 80 patients who underwent cervical laminoplasty for cervical myelopathy. The patients' Japanese Orthopedic Association (JOA) scores (range: 0-17) were analyzed at the 1-, 3-, 6-, and 12-month postoperative timepoints to evaluate their ability to predict the 2-year postoperative outcomes. The patient acceptable symptom state (PASS) was defined as a JOA score ≥ 14.25 at 24 months postoperatively and, ba

SUBMITTER: Seo Y 

PROVIDER: S-EPMC11440713 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature