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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Related Experiment Video

Updated: Jan 26, 2026

Author Spotlight: Unveiling the Potential of TUBE Technique in Spinal Surgery
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Predicting discharge placement after elective surgery for lumbar spinal stenosis using machine learning methods.

Paul T Ogink1, Aditya V Karhade2, Quirina C B S Thio2

  • 1UMC Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands. ptogink@gmail.com.

European Spine Journal : Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
|April 4, 2019
PubMed
Summary

Machine learning accurately predicts non-home discharge after lumbar spinal stenosis surgery. This helps reduce hospital costs and improve planning by identifying patients needing rehabilitation facility or skilled nursing facility (RF/SNF) care.

Keywords:
CalibrationDischarge placementMachine learningRehabilitationSpinal stenosis

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Area of Science:

  • Spinal surgery outcomes research
  • Health informatics
  • Machine learning in healthcare

Background:

  • Delays in patient placement to rehabilitation facilities or skilled nursing facilities (RF/SNF) contribute significantly to total hospitalization time.
  • Accurate prediction of post-surgical RF/SNF needs can optimize resource allocation and reduce healthcare costs.

Purpose of the Study:

  • To develop and evaluate a machine learning algorithm for predicting non-home discharge after elective lumbar spinal stenosis surgery.
  • To identify key patient factors influencing discharge disposition to improve preoperative planning.

Main Methods:

  • Utilized the American College of Surgeons National Surgical Quality Improvement Program database (2009-2016).
  • Developed four machine learning algorithms to predict the primary outcome: non-home discharge.
  • Assessed algorithm performance using discrimination, calibration, and overall performance scores.

Main Results:

  • Included 28,600 patients undergoing elective lumbar spinal stenosis surgery; 18.2% had a non-home discharge.
  • The best-performing model, a neural network, incorporated age, sex, BMI, diabetes, functional status, ASA class, surgical level, fusion, preoperative hematocrit, and creatinine.
  • The neural network achieved a c-statistic of 0.751, indicating good discrimination.

Conclusions:

  • A machine learning algorithm can reliably predict discharge disposition after lumbar spinal stenosis surgery.
  • Clinical implementation of such algorithms may mitigate risks from delayed discharges and reduce healthcare expenditures.
  • This predictive tool supports efficient patient flow and resource management in surgical care pathways.