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ABSTRACT Background Methods Results Conclusion Reliable scientific information is crucial for assessing hip function and evaluating the success of hip surgery. The Harris Hip Score (HHS) is the most widely used tool for measuring hip function and, in particular, the outcomes of hip surgery. The aim of this study was to conduct a systematic review of the literature to identify randomized controlled trials (RCTs) that reported the HHS for hip replacement treatment groups and to test whether there was a substantial difference between reporting only the postoperative HHS or the HHS difference (HHSdiff).PubMed, CNKI, and Epistemonikos were searched until March 1, 2024. The risk of bias, level of evidence, and publication bias were assessed. As HHS is a continuous outcome, mean difference (MD) with 95% confidence intervals (CIs) was calculated using the Hartung–Knapp–Sidik–Jonkman method and a common‐effect/random‐effects model. The same approach was used for both postoperative HHS and HHSdiff. The effect of the two treatment groups studied (minimally invasive vs. conventional approach) on postoperative HHS was then compared with the effect of the two groups studied on the difference in HHS.A total of 41 RCTs, involving 3572 patients, with a low to high risk of bias and a low to moderate publication bias were included. The measured outcome parameters showed a low to moderate level of evidence. There was no relevant difference in the reporting of HHS only postoperatively or HHSdiff when comparing two hip replacement treatment groups in RCTs, measured at 0–0.5, 3, 6, and 12 months postoperatively.The present study showed that there is no relevant difference between reporting of the HHS only postoperatively or HHSdiff when comparing two hip replacement treatment groups in RCTs. Both methods of HHS reporting produced comparable results in an identical cohort of 3765 patients undergoing hip replacement surgery. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is a dynamic area of computer science that is constantly expanding its practical benefits in various fields. The aim of this study was to analyze AI‐guided radiological assessment of femoral neck fractures by performing a systematic review and multilevel meta‐analysis of primary studies. The study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) on May 21, 2024 [CRD42024541055]. The updated Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) guidelines were strictly followed. A systematic literature search of PubMed, Web of Science, Ovid (Med), and Epistemonikos databases was conducted until May 31, 2024. Critical appraisal using the Quality Assessment of Diagnostic Accuracy Studies‐2 (QUADAS‐2) tool showed that the overall quality of the included studies was moderate. In addition, publication bias was presented in funnel plots. A frequentist multilevel meta‐analysis was performed using a random effects model with inverse variance and restricted maximum likelihood heterogeneity estimator with Hartung‐Knapp adjustment. The accuracy between AI‐based and human assessment of femoral neck fractures, sensitivity and specificity with 95% confidence intervals (CIs) were calculated. Study heterogeneity was assessed using the Higgins test I2 (low heterogeneity <25%, moderate heterogeneity: 25%–75%, and high heterogeneity >75%). Finally, 11 studies with a total of 21,163 radiographs were included for meta‐analysis. The results of the study quality assessment using the QUADAS‐2 tool are presented in Table 2. The funnel plots indicated a moderate publication bias. The AI showed excellent accuracy in assessment of femoral neck fractures (Accuracy = 0.91, 95% CI 0.83 to 0.96; I2 = 99%; p < 0.01). The AI showed good sensitivity in assessment of femoral neck fractures (Sensitivity = 0.87, 95% CI 0.77 to 0.93; I2 = 98%; p < 0.01). The AI showed excellent specificity in assessment of femoral neck fractures (Specificity = 0.91, 95% CI 0.77 to 0.97; I2 = 97%; p < 0.01). AI‐guided radiological assessment of femoral neck fractures showed excellent accuracy and specificity as well as good sensitivity. The use of AI as a faster and more reliable assessment tool and as an aid in radiological routine seems justified. [ABSTRACT FROM AUTHOR]

Introduction: Advance care planning (ACP) ensures that patients receive medical care aligned with their values, goals, and preferences, especially regarding end-of-life decisions in serious chronic illnesses., Objective: This project aimed to introduce and promote evidence-based ACP in oncology and palliative care at a midsized hospital near Berlin, Germany, during the COVID-19 pandemic., Methods: This project was guided by the JBI Evidence Implementation Framework and used a mixed methods audit cycle. A baseline audit was conducted using qualitative interviews and workshops with representatives from all the health care disciplines involved in oncology and palliative care at the hospital. The findings were compared with eight best practice recommendations. Targeted strategies aimed at the key stakeholders involved in ACP practice were then implemented. Finally, a semi-quantitative questionnaire was used in a follow-up audit with the same participants as in the baseline audit., Results: The baseline audit revealed a high level of familiarity with the concept of ACP. However, there was a lack of a uniformly accepted definition and understanding of ACP among the health care professionals, leading to a lack of coordination in task distribution. The follow-up audit revealed improvements with regard to education and training in ACP (Criterion 1: 50% to 100%) and organizational support to facilitate ACP conversations (Criterion 3: 87.5% to 100%). Other audit criteria compliance rates remained unchanged., Conclusion: Clinical education and team-based process analysis can facilitate ACP implementation across disciplines in oncology and palliative care facilities. However, the project did not succeed in implementing lasting changes in clinical processes and best practice ACP due to the COVID-19 pandemic. Such an endeavor would demand considerable resources and time, both of which were constrained during the pandemic., Spanish Abstract: http://links.lww.com/IJEBH/A236., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 JBI. Unauthorized reproduction of this article is prohibited.)

Antigravity treadmill training provides a viable option for physiotherapeutic care after knee surgery, especially for conditions that do not allow full weight bearing during the early phase post-intervention. This overview of the current state of knowledge identifies gaps and highlights areas where more research on antigravity treadmill training after knee surgery is needed. This review aimed to analyze and summarize the available evidence concerning the effects of antigravity treadmill training on patients after knee joint surgical procedures, including anterior cruciate ligament reconstruction (ACLR) and total (TKA) and unicompartmental knee arthroplasty (UKA). Several databases were searched for relevant material, including PubMed, Epistemonikos, the Cochrane Library, the Web of Science, and Google Scholar. Seven studies investigating antigravity treadmill training after various procedures were included, including ACLR and TKA. The studies were summarized, and the quality of evidence was evaluated using the appropriate tools. The evidence yielded by these studies suggests that antigravity treadmill training might be useful after knee surgery. However, the superiority over traditional physiotherapeutic measures has yet to be established. Therefore, future high-quality randomized controlled trials (RCTs) are needed to investigate the effect of antigravity treadmill training due to the low quality of available evidence. Also, a cost-effectiveness analysis is required to determine whether the investigated intervention fits the purpose.