{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T02:05:30Z","timestamp":1782180330004,"version":"3.54.5"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T00:00:00Z","timestamp":1630540800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T00:00:00Z","timestamp":1630540800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Arthroplasty"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Patient satisfaction is a unique and important measure of success after total hip arthroplasty (THA). Our study aimed to evaluate the use of machine learning (ML) algorithms to predict patient satisfaction after THA.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Prospectively collected data of 1508 primary THAs performed between 2006 and 2018 were extracted from our joint replacement registry and split into training (80%) and test (20%) sets. Supervised ML algorithms (Random Forest, Extreme Gradient Boosting, Support Vector Machines, Logistic LASSO) were developed with the training set, using patient demographics, comorbidities and preoperative patient reported outcome measures (PROMs) (Short Form-36 [SF-36], physical component summary [PCS] and mental component summary [MCS], Western Ontario and McMaster\u2019s Universities Osteoarthritis Index [WOMAC] and Oxford Hip Score [OHS]) to predict patient satisfaction at 2\u2009years postoperatively. Predictive performance was evaluated using the independent test set.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Preoperative models demonstrated fair discriminative ability in predicting patient satisfaction, with the LASSO model achieving a maximum AUC of 0.76. Permutation importance revealed that the most important predictors of dissatisfaction were (1) patient\u2019s age, (2) preoperative WOMAC, (3) number of comorbidities, (4) preoperative MCS, (5) previous lumbar spine surgery, and (6) low BMI (&lt;\u200918.5).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Machine learning algorithms demonstrated fair discriminative ability in predicting patient satisfaction after THA. We have identified modifiable and non-modifiable predictors of postoperative satisfaction which could enhance preoperative counselling and improve health optimization prior to THA.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s42836-021-00087-3","type":"journal-article","created":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T00:02:44Z","timestamp":1630540964000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Development and internal validation of machine learning algorithms to predict patient satisfaction after total hip arthroplasty"],"prefix":"10.1186","volume":"3","author":[{"given":"Siyuan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jerry Yongqiang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hee Nee","family":"Pang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ngai Nung","family":"Lo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seng Jin","family":"Yeo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4496-8035","authenticated-orcid":false,"given":"Ming Han Lincoln","family":"Liow","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,2]]},"reference":[{"issue":"5","key":"87_CR1","doi-asserted-by":"publisher","first-page":"963","DOI":"10.2106\/00004623-200405000-00012","volume":"86","author":"O Ethgen","year":"2004","unstructured":"Ethgen O, Bruy\u00e8re O, Richy F, Dardennes C, Reginster JY. Health-related quality of life in total hip and total knee arthroplasty. A qualitative and systematic review of the literature. J Bone Joint Surg Am. 2004;86(5):963\u201374. https:\/\/doi.org\/10.2106\/00004623-200405000-00012.","journal-title":"J Bone Joint Surg Am"},{"issue":"11","key":"87_CR2","doi-asserted-by":"publisher","first-page":"3416","DOI":"10.1016\/j.arth.2018.06.034","volume":"33","author":"MP Siljander","year":"2018","unstructured":"Siljander MP, McQuivey KS, Fahs AM, Galasso LA, Serdahely KJ, Karadsheh MS. Current trends in patient-reported outcome measures in Total joint Arthroplasty: a study of 4 major Orthopaedic journals. J Arthroplast. 2018;33(11):3416\u201321. https:\/\/doi.org\/10.1016\/j.arth.2018.06.034.","journal-title":"J Arthroplast"},{"issue":"2","key":"87_CR3","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.arth.2019.09.033","volume":"35","author":"MJ Halawi","year":"2020","unstructured":"Halawi MJ, Jongbloed W, Baron S, Savoy L, Cote MP, Lieberman JR. Patient-reported outcome measures are not a valid proxy for patient satisfaction in Total joint Arthroplasty. J Arthroplast. 2020;35(2):335\u20139. https:\/\/doi.org\/10.1016\/j.arth.2019.09.033.","journal-title":"J Arthroplast"},{"issue":"4","key":"87_CR4","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1016\/s0883-5403(97)90194-7","volume":"12","author":"CA Mancuso","year":"1997","unstructured":"Mancuso CA, Salvati EA, Johanson NA, Peterson MG, Charlson ME. Patients\u2019 expectations and satisfaction with total hip arthroplasty. J Arthroplast. 1997;12(4):387\u201396. https:\/\/doi.org\/10.1016\/s0883-5403(97)90194-7.","journal-title":"J Arthroplast"},{"issue":"2","key":"87_CR5","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.arth.2010.03.013","volume":"26","author":"RE Anakwe","year":"2011","unstructured":"Anakwe RE, Jenkins PJ, Moran M. Predicting dissatisfaction after total hip arthroplasty: a study of 850 patients. J Arthroplast. 2011;26(2):209\u201313. https:\/\/doi.org\/10.1016\/j.arth.2010.03.013.","journal-title":"J Arthroplast"},{"issue":"1","key":"87_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2474-15-53","volume":"15","author":"C Palazzo","year":"2014","unstructured":"Palazzo C, Jourdan C, Descamps S, Nizard R, Hamadouche M, Anract P, et al. Determinants of satisfaction 1 year after total hip arthroplasty: the role of expectations fulfilment. BMC Musculoskelet Disord. 2014;15(1):1\u20139. https:\/\/doi.org\/10.1186\/1471-2474-15-53.","journal-title":"BMC Musculoskelet Disord"},{"issue":"12","key":"87_CR7","doi-asserted-by":"publisher","first-page":"3854","DOI":"10.1016\/j.arth.2017.07.021","volume":"32","author":"R Gunaratne","year":"2017","unstructured":"Gunaratne R, Pratt DN, Banda J, Fick DP, Khan RJK, Robertson BW. Patient dissatisfaction following Total knee Arthroplasty: a systematic review of the literature. J Arthroplast. 2017;32(12):3854\u201360. https:\/\/doi.org\/10.1016\/j.arth.2017.07.021.","journal-title":"J Arthroplast"},{"issue":"6","key":"87_CR8","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1097\/CORR.0000000000000687","volume":"477","author":"MA Fontana","year":"2019","unstructured":"Fontana MA, Lyman S, Sarker GK, Padgett DE, MacLean CH. Can machine learning algorithms predict which patients will achieve minimally clinically important differences from Total joint Arthroplasty? Clin Orthop Relat Res. 2019;477(6):1267\u201379. https:\/\/doi.org\/10.1097\/CORR.0000000000000687.","journal-title":"Clin Orthop Relat Res"},{"issue":"1","key":"87_CR9","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s12911-018-0731-6","volume":"19","author":"M Huber","year":"2019","unstructured":"Huber M, Kurz C, Leidl R. Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning. BMC Med Inform Decis Mak. 2019;19(1):3. https:\/\/doi.org\/10.1186\/s12911-018-0731-6.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"9","key":"87_CR10","doi-asserted-by":"publisher","first-page":"2458","DOI":"10.1016\/j.arth.2020.04.034","volume":"35","author":"MGHGG Yeo","year":"2020","unstructured":"Yeo MGHGG, Chen JY, Lo NN, Yeo SJ, Liow MHL. Are Oxford hip score and Western Ontario and McMaster universities osteoarthritis index useful predictors of clinical meaningful improvement and satisfaction after Total hip Arthroplasty? J Arthroplast. 2020;35(9):2458\u201364. https:\/\/doi.org\/10.1016\/j.arth.2020.04.034.","journal-title":"J Arthroplast"},{"issue":"9","key":"87_CR11","doi-asserted-by":"publisher","first-page":"769","DOI":"10.2106\/JBJS.N.00841","volume":"97","author":"DH Wei","year":"2015","unstructured":"Wei DH, Hawker GA, Jevsevar DS, Bozic KJ. Improving value in musculoskeletal care delivery: AOA critical issues. J Bone Joint Surg Am. 2015;97(9):769\u201374. https:\/\/doi.org\/10.2106\/JBJS.N.00841.","journal-title":"J Bone Joint Surg Am"},{"issue":"8","key":"87_CR12","doi-asserted-by":"publisher","first-page":"e685","DOI":"10.3928\/01477447-20150804-55","volume":"38","author":"MJ Halawi","year":"2015","unstructured":"Halawi MJ. Outcome measures in Total joint Arthroplasty: current status, challenges, and future directions. Orthopedics. 2015;38(8):e685\u20139. https:\/\/doi.org\/10.3928\/01477447-20150804-55.","journal-title":"Orthopedics"},{"issue":"1","key":"87_CR13","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1056\/NEJMp1209500","volume":"368","author":"EOEE Lee","year":"2013","unstructured":"Lee EOEE. Shared decision making to improve care and reduce costs. N Engl J Med. 2013;368(1):6\u20138. https:\/\/doi.org\/10.1056\/NEJMp1209500.","journal-title":"N Engl J Med"},{"issue":"10","key":"87_CR14","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.1007\/s11606-012-2077-6","volume":"27","author":"G Elwyn","year":"2012","unstructured":"Elwyn G, Frosch D, Thomson R, Joseph-Williams N, Lloyd A, Kinnersley P, et al. Shared decision making: a model for clinical practice. J Gen Intern Med. 2012;27(10):1361\u20137. https:\/\/doi.org\/10.1007\/s11606-012-2077-6.","journal-title":"J Gen Intern Med"},{"issue":"8","key":"87_CR15","doi-asserted-by":"publisher","first-page":"2358","DOI":"10.1016\/j.arth.2018.02.067","volume":"33","author":"SA Bini","year":"2018","unstructured":"Bini SA. Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care? J Arthroplast. 2018;33(8):2358\u201361. https:\/\/doi.org\/10.1016\/j.arth.2018.02.067.","journal-title":"J Arthroplast"},{"issue":"1","key":"87_CR16","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1093\/cid\/cix731","volume":"66","author":"J Wiens","year":"2018","unstructured":"Wiens J, Shenoy ES. Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clin Infect Dis. 2018;66(1):149\u201353. https:\/\/doi.org\/10.1093\/cid\/cix731.","journal-title":"Clin Infect Dis"},{"issue":"5","key":"87_CR17","doi-asserted-by":"publisher","first-page":"e262","DOI":"10.1016\/S1470-2045(19)30149-4","volume":"20","author":"KY Ngiam","year":"2019","unstructured":"Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019;20(5):e262\u201373. https:\/\/doi.org\/10.1016\/S1470-2045(19)30149-4.","journal-title":"Lancet Oncol"},{"issue":"12","key":"87_CR18","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1016\/j.rec.2019.05.014","volume":"72","author":"PI Dorado-D\u00edaz","year":"2019","unstructured":"Dorado-D\u00edaz PI, Sampedro-G\u00f3mez J, Vicente-Palacios V, S\u00e1nchez PL. Applications of artificial intelligence in cardiology. The future is already here. Rev Esp Cardiol (Engl Ed). 2019;72(12):1065\u201375. https:\/\/doi.org\/10.1016\/j.rec.2019.05.014.","journal-title":"Rev Esp Cardiol (Engl Ed)"},{"issue":"13","key":"87_CR19","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1001\/jama.2017.18391","volume":"319","author":"AL Beam","year":"2018","unstructured":"Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317\u20138. https:\/\/doi.org\/10.1001\/jama.2017.18391.","journal-title":"JAMA"},{"issue":"8","key":"87_CR20","doi-asserted-by":"publisher","first-page":"2119","DOI":"10.1016\/j.arth.2020.03.019","volume":"35","author":"KN Kunze","year":"2020","unstructured":"Kunze KN, Karhade AV, Sadauskas AJ, Schwab JH, Levine BR. Development of machine learning algorithms to predict clinically meaningful improvement for the patient-reported health state after Total hip Arthroplasty. J Arthroplast. 2020;35(8):2119\u201323. https:\/\/doi.org\/10.1016\/j.arth.2020.03.019.","journal-title":"J Arthroplast"},{"issue":"11","key":"87_CR21","doi-asserted-by":"publisher","first-page":"3117","DOI":"10.1016\/j.arth.2020.05.061","volume":"35","author":"KN Kunze","year":"2020","unstructured":"Kunze KN, Polce EM, Sadauskas AJ, Levine BR. Development of machine learning algorithms to predict patient dissatisfaction after primary Total knee Arthroplasty. J Arthroplast. 2020;35(11):3117\u201322. https:\/\/doi.org\/10.1016\/j.arth.2020.05.061.","journal-title":"J Arthroplast"},{"issue":"11","key":"87_CR22","doi-asserted-by":"publisher","first-page":"3123","DOI":"10.1016\/j.arth.2020.05.077","volume":"35","author":"H Farooq","year":"2020","unstructured":"Farooq H, Deckard ER, Ziemba-Davis M, Madsen A, Meneghini RM. Predictors of patient satisfaction following primary Total knee Arthroplasty: results from a traditional statistical model and a machine learning algorithm. J Arthroplast. 2020;35(11):3123\u201330. https:\/\/doi.org\/10.1016\/j.arth.2020.05.077.","journal-title":"J Arthroplast"},{"issue":"1","key":"87_CR23","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s11999-009-1119-9","volume":"468","author":"RB Bourne","year":"2010","unstructured":"Bourne RB, Chesworth BM, Davis AM, Mahomed NN, Charron KDJ. Patient satisfaction after total knee arthroplasty: who is satisfied and who is not? Clin Orthop Relat Res. 2010;468(1):57\u201363. https:\/\/doi.org\/10.1007\/s11999-009-1119-9.","journal-title":"Clin Orthop Relat Res"},{"issue":"19","key":"87_CR24","doi-asserted-by":"publisher","first-page":"1628","DOI":"10.2106\/JBJS.O.00030","volume":"97","author":"NC Laucis","year":"2015","unstructured":"Laucis NC, Hays RD, Bhattacharyya T. Scoring the SF-36 in Orthopaedics: a brief guide. J Bone Joint Surg Am. 2015;97(19):1628\u201334. https:\/\/doi.org\/10.2106\/JBJS.O.00030.","journal-title":"J Bone Joint Surg Am"},{"issue":"8","key":"87_CR25","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1055\/s-0038-1666866","volume":"32","author":"LC Walker","year":"2019","unstructured":"Walker LC, Clement ND, Deehan DJ. Predicting the outcome of Total knee Arthroplasty using the WOMAC score: a review of the literature. J Knee Surg. 2019;32(8):736\u201341. https:\/\/doi.org\/10.1055\/s-0038-1666866.","journal-title":"J Knee Surg"},{"key":"87_CR26","doi-asserted-by":"publisher","first-page":"S200","DOI":"10.1002\/acr.20549","volume":"63","author":"ABA Nilsdotter","year":"2011","unstructured":"Nilsdotter ABA. Measures of hip function and symptoms: Harris hip score (HHS), hip disability and osteoarthritis outcome score (HOOS), Oxford hip score (OHS), lequesne index of severity for osteoarthritis of the hip (LISOH), and American Academy of orthopedic surgeons (AAOS) hip and knee questionnaire. Arthritis Care Res. 2011;63:S200\u20137.","journal-title":"Arthritis Care Res"},{"issue":"1","key":"87_CR27","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/1007730.1007735","volume":"6","author":"GEAPA Batista","year":"2004","unstructured":"Batista GEAPA, Prati RC, Monard MC. A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor Newsl. 2004;6(1):20\u20139. https:\/\/doi.org\/10.1145\/1007730.1007735.","journal-title":"SIGKDD Explor Newsl"},{"key":"87_CR28","unstructured":"Cava W, Bauer C, Moore JH, Pendergrass SA. Interpretation of machine learning predictions for patient outcomes in electronic health records. AMIA Annu Symp Proc. 2019;2020:572\u201381."},{"issue":"4","key":"87_CR29","doi-asserted-by":"publisher","first-page":"e002525","DOI":"10.1136\/bmjopen-2012-002525","volume":"3","author":"DF Hamilton","year":"2013","unstructured":"Hamilton DF, Lane JV, Gaston P, Patton JT, Macdonald D, Simpson AH, et al. What determines patient satisfaction with surgery? A prospective cohort study of 4709 patients following total joint replacement. BMJ Open. 2013;3(4):e002525. https:\/\/doi.org\/10.1136\/bmjopen-2012-002525.","journal-title":"BMJ Open"},{"issue":"4","key":"87_CR30","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1302\/0301-620X.103B4.BJJ-2020-0629.R3","volume":"103-B","author":"JJ Tolk","year":"2021","unstructured":"Tolk JJ, Janssen RPA, Haanstra TM, van der Steen MC, Bierma-Zeinstra SMA, Reijman M. The influence of expectation modification in knee arthroplasty on satisfaction of patients: a randomized controlled trial. Bone Joint J. 2021 Apr;103-B(4):619\u201326. https:\/\/doi.org\/10.1302\/0301-620X.103B4.BJJ-2020-0629.R3.","journal-title":"Bone Joint J"},{"issue":"7","key":"87_CR31","doi-asserted-by":"publisher","first-page":"567","DOI":"10.2106\/JBJS.20.00169","volume":"103","author":"X Geng","year":"2021","unstructured":"Geng X, Wang X, Zhou G, Li F, Li Y, Zhao M, et al. A randomized controlled trial of psychological intervention to improve satisfaction for patients with depression undergoing TKA: a 2-year follow-up. J Bone Joint Surg Am. 2021;103(7):567\u201374. https:\/\/doi.org\/10.2106\/JBJS.20.00169.","journal-title":"J Bone Joint Surg Am"},{"issue":"2","key":"87_CR32","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.arth.2007.11.005","volume":"24","author":"TK Kim","year":"2009","unstructured":"Kim TK, Chang CB, Kang YG, Kim SJ, Seong SC. Causes and predictors of patient's dissatisfaction after uncomplicated total knee arthroplasty. J Arthroplast. 2009;24(2):263\u201371. https:\/\/doi.org\/10.1016\/j.arth.2007.11.005.","journal-title":"J Arthroplast"},{"issue":"12","key":"87_CR33","doi-asserted-by":"publisher","first-page":"2415","DOI":"10.3899\/jrheum.080295","volume":"35","author":"R Gandhi","year":"2008","unstructured":"Gandhi R, Davey JR, Mahomed NN. Predicting patient dissatisfaction following joint replacement surgery. J Rheumatol. 2008;35(12):2415\u20138. https:\/\/doi.org\/10.3899\/jrheum.080295.","journal-title":"J Rheumatol"},{"issue":"7","key":"87_CR34","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1302\/0301-620X.101B7.BJJ-2018-1188.R1","volume":"101-B","author":"N Eftekhary","year":"2019","unstructured":"Eftekhary N, Shimmin A, Lazennec JY, Buckland A, Schwarzkopf R, Dorr LD, et al. A systematic approach to the hip-spine relationship and its applications to total hip arthroplasty. Bone Joint J. 2019;101-B(7):808\u201316. https:\/\/doi.org\/10.1302\/0301-620X.101B7.BJJ-2018-1188.R1.","journal-title":"Bone Joint J"},{"issue":"18","key":"87_CR35","doi-asserted-by":"publisher","first-page":"1606","DOI":"10.2106\/JBJS.17.00403","volume":"100","author":"H Ike","year":"2018","unstructured":"Ike H, Dorr LD, Trasolini N, Stefl M, McKnight B, Heckmann N. Spine-pelvis-hip relationship in the functioning of a Total hip replacement. J Bone Joint Surg Am. 2018;100(18):1606\u201315. https:\/\/doi.org\/10.2106\/JBJS.17.00403.","journal-title":"J Bone Joint Surg Am"},{"issue":"10","key":"87_CR36","doi-asserted-by":"publisher","first-page":"2085","DOI":"10.1016\/j.arth.2016.03.015","volume":"31","author":"Z Sayeed","year":"2016","unstructured":"Sayeed Z, Anoushiravani AA, Chambers MC, Gilbert TJ, Scaife SL, El-Othmani MM, et al. Comparing in-hospital Total joint Arthroplasty outcomes and resource consumption among underweight and morbidly obese patients. J Arthroplast. 2016;31(10):2085\u201390. https:\/\/doi.org\/10.1016\/j.arth.2016.03.015.","journal-title":"J Arthroplast"},{"issue":"1","key":"87_CR37","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.arth.2020.07.055","volume":"36","author":"A Katakam","year":"2021","unstructured":"Katakam A, Melnic CM, Bragdon CR, Sauder N, Collins AK, Bedair HS. Low body mass index is a predictor for mortality and increased length of stay following Total joint Arthroplasty. J Arthroplast. 2021;36(1):72\u20137. https:\/\/doi.org\/10.1016\/j.arth.2020.07.055.","journal-title":"J Arthroplast"}],"container-title":["Arthroplasty"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42836-021-00087-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42836-021-00087-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42836-021-00087-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T00:03:37Z","timestamp":1630541017000},"score":1,"resource":{"primary":{"URL":"https:\/\/arthroplasty.biomedcentral.com\/articles\/10.1186\/s42836-021-00087-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,2]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["87"],"URL":"https:\/\/doi.org\/10.1186\/s42836-021-00087-3","relation":{},"ISSN":["2524-7948"],"issn-type":[{"value":"2524-7948","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,2]]},"assertion":[{"value":"2 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Seng Jin Yeo is a member of the Editorial Board of <i>Arthroplasty<\/i> and other authors declare that they have no competing interests. All authors were not involved in the journal\u2019s review of or decisions related to, this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Ethics approval (CIRB: 2020\/2843) was obtained prior to initiation of study.Waiver of informed consent was obtained from the ethics board as de-identified data from the joint replacement registry\u00a0were\u00a0used.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"33"}}