Reply to the Letter to the Editor: Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?
- PMID: 32271173
- PMCID: PMC7319377
- DOI: 10.1097/CORR.0000000000001255
Reply to the Letter to the Editor: Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?
Comment on
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Reply to the Letter to the Editor: Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?Clin Orthop Relat Res. 2020 Jun;478(6):1376-1377. doi: 10.1097/CORR.0000000000001227. Clin Orthop Relat Res. 2020. PMID: 32187104 Free PMC article. No abstract available.
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Letter to the Editor: Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?Clin Orthop Relat Res. 2020 Jun;478(6):1374-1375. doi: 10.1097/CORR.0000000000001226. Clin Orthop Relat Res. 2020. PMID: 32195714 Free PMC article. No abstract available.
References
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- Ghomrawi HM, Mancuso CA, Dunning A, Gonzalez Della Valle A, Alexiades M, Cornell C, Sculco T, Bostrom M, Mayman D, Marx RG, Westrich G, O'Dell M, Mushlin AI. Do surgeon expectations predict clinically important improvements in WOMAC scores after THA and TKA? Clin Orthop Relat Res. 2017;475:2150–2158. - PMC - PubMed
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- Katz JN, Winter AR, Hawker G. Measures of the appropriateness of elective orthopaedic joint and spine procedures. J Bone Joint Surg Am. 2017;99:e15. - PubMed
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