目的 探讨 Oxford 3 代膝关节单髁置换（UKA）假体型号的分布特点，为临床选择合适UKA假体提供参考。 方法 对治疗组2019年4月至2023年9月期间，用Oxford 3代假体行UKA且符合纳入标准的270例患者资料进行回顾性分析。记录患者性别、年龄、身高、体质量、侧别及假体型号，分析股骨和胫骨假体型号的分布与匹配关系。用多元有序logistic回归和ROC曲线分析影响股骨型号的关键因素，构建UKA假体型号相关参数的分布图。 结果 股骨假体中XS（21.5%）、S（44.1%）、M（26.3%）、L（8.1%）；胫骨假中AA（11.1%）、A（23.7%）、B（29.3%）、C（18.9%）、D（10.4%）、E（6.7%）。胫骨AA对应股骨XS符合率93.3%，A/B对应S符合率96.0%，C/D对应M符合率95.8%，E对应L符合率91.7%，总体匹配率95.2%。身高（AUC = 90.63%）、性别（AUC = 81.59%）及体质量（AUC = 84.47%）与股骨型号显著相关（P < 0.01），年龄和侧别差异无统计学意义。女性多使用S号（59.6%），男性以M/L号为主（65.2%）；女性≤ 160 cm 时XS/S号占比超90%，男性> 175 cm时L号使用率达88.9%。基于身高、性别术前预测的准确率为70.7%，术中结合胫骨型号后提升至77.0%。 结论 本研究人群UKA假体分布与西方人群存在差异，与亚洲及国内其他地区也有所不同。本研究建立的UKA假体分布图及匹配规则预测准确度较高，能有效指导术前规划与术中股骨假体型号的选择。

Objective To investigate the distribution characteristics of model numbers associated with the third-generation Oxford unicompartmental knee arthroplasty （UKA） prostheses， in order to provide evidence-based guidance for the selection of appropriate UKA prostheses in clinical practice. Methods A retrospective analysis was conducted on data from 270 patients who underwent UKA using third-generation Oxford prostheses and fulfilled the inclusion criteria between April 2019 and September 2023. Patient characteristics， including gender， age， height， weight， affected side， and prosthesis model numbers， were collected and recorded. The distribution and matching patterns of femoral and tibial prosthesis model numbers were evaluated. Multiple ordinal logistic regression analyses and receiver operating characteristic （ROC） curves were employed to identify significant predictors influencing the selection of femoral component models. Additionally， a parameter distribution map related to UKA prosthesis model numbers was developed. Results Among the femoral prostheses， the XS size accounted for 21.5%， S for 44.1%， M for 26.3%， and L for 8.1%. Regarding tibial prostheses， AA represented 11.1%， A 23.7%， B 29.3%， C 18.9%， D 10.4%， and E 6.7%. The matching rates between tibial and femoral components were as follows： 93.3% for tibial AA corresponding to femoral XS， 96.0% for tibial A/B corresponding to femoral S， 95.8% for tibial C/D corresponding to femoral M， and 91.7% for tibial E corresponding to femoral L， resulting in an overall matching rate of 95.2%. Height （AUC = 90.63%）， gender （AUC = 81.59%）， and weight （AUC = 84.47%） showed significant correlations with femoral model selection （P < 0.01）， whereas age and side did not demonstrate statistical significance. The S femoral model was predominantly used in female patients （59.6%）， while the M and L models were more commonly utilized in male patients （65.2%）. When female patients' height was ≤160 cm， the combined usage rate of the XS and S models exceeded 90%. For male patients taller than 175 cm， the utilization rate of the L model reached 88.9%. The accuracy rate of preoperative prediction based on height and gender was 70.7%， which improved to 77.0% when incorporating intraoperative tibial model information. Conclusions The distribution of UKA prostheses in the study population differed from that observed in Western populations， as well as from other regions in Asia and China. The distribution map of UKA prostheses and the corresponding matching rules established in this study demonstrate a high level of predictive accuracy， which can effectively support preoperative planning and intraoperative selection of femoral prosthesis model numbers.