Purpose To present the antibiotic prescription trend between 2011–2018 at primary healthcare in Turkey in order to evaluate the effects of interventions at national level for providing rational prescription of antibiotics. Methods Electronic prescription data of the family physicians collected from January 1, 2011 to December 31, 2018 in 81 provinces of Turkey were recorded through the Prescription Information System and screened for the antimicrobial drugs. The interventions to promote rational antibiotic use during 2011–2018 in Turkey includes reminding the legislation to stop access of antibiotics without prescription, monitoring of antibiotic prescription behaviors of primary healthcare physicians, and education of healthcare workers and the public on the appropriate use of antibiotics. Results A total of 1 054 261 396 prescriptions for outpatients of all age groups were recorded during this period. Of the prescriptions written by family physcians, 34.94% were containing at least one antibiotic in 2011, which declined to 24.55% in 2018. Antibiotics constituted 13.99% of all the items in prescriptions in 2011 and 10.47% in 2018. Percentage of total antibiotic expenditure to the total drug expanditure decreased from 14.14% to 4.12% during 2011–2018. The most commonly prescribed antibiotics were amoxicillin and enzyme inhibitor combination, cefdinir, and cefuroxime during 2011–2018, with an increasing trend for prescription of first‐line antibiotic, amoxicillin, in recent years. Conclusions Governmental interventions at national level have contributed to reducing antibiotic prescription and increasing preference of first‐line antibiotics at primary healthcare level in Turkey over a course of 8 years. Turkey's model of governmental interventions may set an example for other countries with high consumption of antibiotics, and contribute to the actions against antimicrobial resistance worldwide.

Objective of this study is to assess the pullout performance of various pedicle screws in different test materials after toggling tests comparatively. Solid core, cannulated (cemented), novel expandable and solid-core (cemented) pedicle screws were instrumented to the polyurethane foams (Grade 10 and Grade 40) produced in laboratory and bovine vertebra. ASTM F543 standard was used for preparation process of samples. Toggling tests were carried out. After toggling test procedures, pullout tests were performed. Load versus displacement graph was recorded, and the ultimate pullout force was defined as the maximum load (pullout strength) sustained before failure of screw. Anteriosuperior and oblique radiographs were taken from each sample after instrumentation in order to examine screw placement and cement distribution. The pullout strength of pedicle screws decreased after toggling tests with respect to the initial condition. While the cemented solid-core pedicle screws had the highest pullout strength in all test materials, they had the highest strength differences. The cemented solid-core pedicle screws had decrement rates of 27% and 16% in Grade 10 and Grade 40, respectively. There are almost same decrement rate (between 5.5% and 6.5%) for all types of pedicle screws instrumented to the samples of bovine vertebra. The pullout strengths of novel expandable pedicle screws in both of early period and after toggling conditions were almost similar, in other words, the decrement rates of it were lower than other types. According to the data collected from this study, polymethylmethacrylate augmentation significantly decreases pullout strength following the toggling loads. Higher brittleness of cured polymethylmethacrylate has adverse effect on the pullout strength. Although augmentation is an important process for enhancing pullout strength in early period, it has some disadvantages for preserving stabilization in a long time. Expandable pedicle screw with polyetheretherketone shell may be good alternative to polymethylmethacrylate augmentation on both primer stabilization and long-term loading application with toggling.

Fractures occur in the shoulder area, which has a wider range of motion than other joints in the body, for various reasons. To diagnose these fractures, data gathered from X-radiation (X-ray), magnetic resonance imaging (MRI), or computed tomography (CT) are used. This study aims to help physicians by classifying shoulder images taken from X-ray devices as fracture/non-fracture with artificial intelligence. For this purpose, the performances of 26 deep learning-based pre-trained models in the detection of shoulder fractures were evaluated on the musculoskeletal radiographs (MURA) dataset, and two ensemble learning models (EL1 and EL2) were developed. The pre-trained models used are ResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet, and their spinal fully connected (Spinal FC) versions. In the EL1 and EL2 models developed using pre-trained models with the best performance, test accuracy was 0.8455, 0.8472, Cohen’s kappa was 0.6907, 0.6942 and the area that was related with fracture class under the receiver operating characteristic (ROC) curve (AUC) was 0.8862, 0.8695. As a result of 28 different classifications in total, the highest test accuracy and Cohen’s kappa values were obtained in the EL2 model, and the highest AUC value was obtained in the EL1 model.

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