Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 8, 2026Acta OrthopaedicaOpen Access

Response to Letter: Analytic approaches for prognostic studies of persistent pain following knee arthroplasty

View Full Paper
Ask AI
Bookmark
Share

Authors

ARAnni RajamäkiARAleksi ReitoMKMari Karsikas

Discussion

Loading...

Member takes

Overview

Prognostic modeling analysis reveals complex algorithms fail to improve pain prediction after knee arthroplasty, indicating standard preoperative variables lack key predictive information.

Key Points

  • To assess whether utilizing more complex machine-learning algorithms improves the prediction of persistent pain and poor functional outcomes following total knee arthroplasty.
  • Tested predictive performance across 3 distinct machine-learning algorithms of increasing methodological complexity.
  • Utilized routinely collected preoperative clinical variables to model postoperative persistent pain and functional outcomes.
  • Increasing algorithmic and methodological complexity did not improve predictive performance (AUC) in the dataset.
  • Routinely collected preoperative clinical variables lacked sufficient informational value to accurately account for residual pain and poor postoperative function.

Cite This Study

Rajamäki et al. (2026) studied this question.

synapsesocial.com/papers/6a9fd80458e84d0ff5b47197https://doi.org/10.2340/17453674.2026.46806
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Classification and stratification of patient pain archetypes following total knee arthroplasty: a machine learning approach2026 · 1 citations
  2. 2Joint‐specific measures improve risk adjustment in total knee arthroplasty: A machine learning approach2026
  3. 3A development of machine learning models to preoperatively predict insufficient clinical improvement after total knee arthroplasty2025 · 6 citations
  4. 4Machine Learning Using Preoperative Patient Factors Can Predict the Severity of Pain Following Primary Total Hip Arthroplasty2026
  5. 5Prediction of Early Adverse Events After <scp>THA</scp>: A Comparison of Different Machine‐Learning Strategies Based on 262,356 Observations From the Nordic Arthroplasty Register Association (<scp>NARA</scp>) Dataset2024 · 1 citations