RESUMO
Objective: This study aims to develop neural networks to detect hormone secretion profiles in the pituitary adenomas based on T2 weighted magnetic resonance imaging (MRI) radiomics. Methods: This retrospective model-development study included a cohort of patients with pituitary adenomas (n=130) from January 2015 to January 2020 in one tertiary center. The mean age was 46.49±13.69 years, and 76/130 (58.46%) were women. Three observers segmented lesions on coronal T2 weighted MRI, and an interrater agreement was evaluated using the Dice coefficient. Predictors were determined as radiomics features (n=851). Feature selection was based on intraclass correlation coefficient, coefficient variance, variance inflation factor, and LASSO regression analysis. Outcomes were identified as 7 hormone secretion profiles [non-functioning pituitary adenoma, growth hormone-secreting adenomas, prolactinomas, adrenocorticotropic hormone-secreting adenomas, pluri-hormonal secreting adenomas (PHA), follicle-stimulating hormone and luteinizing hormone-secreting adenomas, and thyroid-stimulating hormone adenomas]. A multivariable diagnostic prediction model was developed with artificial neural networks (ANN) for 7 outcomes. ANN performance was presented as an area under the receiver operating characteristic curve (AUC) and accepted as successful if the AUC was >0.85 and p-value was <0.01. Results: The performance of the ANN distinguishing prolactinomas from other adenomas was validated (AUC=0.95, p<0.001, sensitivity: 91%, and specificity: 98%). The model distinguishing PHA had the lowest AUC (AUC=0.74 and p<0.001). The AUC values for the other five ANN were >0.85 and p values were <0.001. Conclusions: This study was successful in training neural networks that could differentiate the hormone secretion profile of pituitary adenomas.
RESUMO
Objective: The aim of this study is to evaluate the pharmacists' ability to use pMDIs with a spacer device and the factors that affect this ability.Method: Face to face interviews were conducted with the pharmacists. A nine item questionnaire was completed and the checklist for how to use pMDIs with a spacer device was filled out.Results: A total of 307 pharmacists voluntarily participated in this study. Fifty-six (18.2%) of the pharmacists stated that they did not know how to use pMDIs with a spacer device. These pharmacists were excluded and remaining 251 pharmacists included in the study. Only 100 (39.8%) pharmacists demonstrated all of the inhaler spacer device usage steps correctly. The step in which pharmacists made the most mistakes was "take 5-6 deep and slow breaths, hold for 10 s and slow breaths." Those pharmacists who were more likely to correctly use pMDIs with a spacer device were younger (p = 0.023), had dispensed more asthma medications per day (p < 0.001), had dispensed more asthma medications per day for patients younger than six years of age (p = 0.016), and sold inhaler spacer devices at their pharmacy (p = 0.042).Conclusion: Approximately one third of the pharmacists in the current study were able to correctly demonstrate all of the steps for proper usage of pMDIs with a spacer device, which indicates that pharmacists should be included in the training program and be provided continuous training on the use of pMDIs with a spacer device.
