Boyle Johannesen (beadsilica5)

This study aimed to evaluate both publication and authorship characteristics in Knee Surgery, Sports Traumatology, Arthroscopy journal (KSSTA) regarding knee arthroplasty over the past 15years. PubMed was searched for articles published in KSSTA between January 1, 2006, and December 31st, 2020, utilising the search term 'knee arthroplasty'. 1288 articles met the inclusion criteria. The articles were evaluated using the following criteria type of article, type of study, main topic and special topic, use of patient-reported outcome scores, number of references and citations, level of evidence (LOE), number of authors, gender of the first author and continent of origin. Three time intervals were compared 2006-2010, 2011-2015 and 2016-2020. Between 2016 and 2020, publications peaked at 670 articles (52%) compared with 465 (36%) published between 2011 and 2016 and 153 articles (12%) between 2006 and 2010. While percentage of reviews (2006-2010 0% vs. 2011-2015 5% vs. 2016-2020 5%) and meta-analyses (1% vs. 6e latest techniques at each time interval. With rising number of authors, the part of female first authors also increased-but not significantly. Furthermore, publishing characteristics showed an increasing number of publications from Asia and a slightly decreasing number in Europe. IV. IV. Unicompartmental Knee Arthroplasty (UKA) recorded an increased incidence of around 30% per year in the United States. Patient's experience and satisfaction after surgery were traditionally assessed by pre, and post-surgical scores and Patient-Reported Outcome Measures (PROMs) scales. Traditional scales as Western Ontario and McMaster University Osteoarthritis Index (WOMAC) and Oxford Knee Score (OKS) reported high ceiling effect. Patients treated by UKA usually perform well; therefore, it is necessary to have a PROMs' scale with a low ceiling effect as the Forgotten Joint Score-12 (FJS-12). PROMs have to be validated in the local language to be used. This study aims to perform a psychometric validation of the Italian version of FJS-12 for UKA for the first time. Between January 2019 and October 2019, 44 patients were included. Each patient completed both the FJS-12 Italian version and the WOMAC Italian version in preoperative follow-up, after 2-week and 1-month, 3-month, and 6-month postoperative follow-u accurate studies on outcomes after UKA. Level III, diagnostic study. Level III, diagnostic study. To evaluate whether a deep learning model (DLM) could increase the detection sensitivity of radiologists for intracranial aneurysms on CT angiography (CTA) in aneurysmal subarachnoid hemorrhage (aSAH). Three different DLMs were trained on CTA datasets of 68 aSAH patients with 79 aneurysms with their outputs being combined applying ensemble learning (DLM-Ens). The DLM-Ens was evaluated on an independent test set of 104 aSAH patients with 126 aneuryms (mean volume 129.2 ± 185.4 mm , 13.0% at the posterior circulation), which were determined by two radiologists and one neurosurgeon in consensus using CTA and digital subtraction angiography scans. CTA scans of the test set were then presented to three blinded radiologists (reader 1 13, reader 2 4, and reader 3 3 years of experience in diagnostic neuroradiology), who assessed them individually for aneurysms. Detection sensitivities for aneurysms of the readers with and without the assistance of the DLM were compared. In the test set, the detection sensitivity of the DLM-Ens (85.7%) was comparable to the radiologists (reader 1 91.2%, reader 2 86.5%, and reader 3 86.5%; Fleiss κ of 0.502). DLM-assistance significantly increased the detection sensitivity (reader 1 97.6%, reader 2 97.6%,and reader 3 96.0%; overall P=.024; Fleiss κ of 0.878), especially for secondary aneurysms (88.2% of the additional aneurysms provided by the DLM). Deep learning significantly improved the detection