Dark room disease

'Dark room' disease describes a collection of symptoms that some healthcare workers experience when exposed to film-processing chemicals. The purpose of this article is to examine the symptoms that are relevant to the use of these substances by radiology technicians (RT) and to investigate the practical precautions that should be taken by the employer and the radiographer in order to protect the radiographer from developing the 'dark room' disease

Sick-building syndrome

In the present article we study, from theoretical point of view, the sick-building syndrome (SBS). We analyse more than 50 symptoms that are reported by those who suffer from the SBS. In addition, the most important factors that lead to the development of SBS are distinguished and we mention the substances that generally seem to cause SBS. Moreover, a model about the prediction and ascertainment of SBS has been proposed. Finally, overall rules for prevention and confrontation of this problem are suggested

Using machine learning and big data for the prediction of venous thromboembolic events after spine surgery: A single-center retrospective analysis of multiple models on a cohort of 6869 patients

Objective: Venous thromboembolic event (VTE) after spine surgery is a rare but potentially devastating complication. With the advent of machine learning, an opportunity exists for more accurate prediction of such events to aid in prevention and treatment. Methods: Seven models were screened using 108 database variables and 62 preoperative variables. These models included deep neural network (DNN), DNN with synthetic minority oversampling technique (SMOTE), logistic regression, ridge regression, lasso regression, simple linear regression, and gradient boosting classifier. Relevant metrics were compared between each model. The top four models were selected based on area under the receiver operator curve; these models included DNN with SMOTE, linear regression, lasso regression, and ridge regression. Separate random sampling of each model was performed 1000 additional independent times using a randomly generated training/testing distribution. Variable weights and magnitudes were analyzed after sampling. Results: Using all patient-related variables, DNN using SMOTE was the top-performing model in predicting postoperative VTE after spinal surgery (area under the curve [AUC] =0.904), followed by lasso regression (AUC = 0.894), ridge regression (AUC = 0.873), and linear regression (AUC = 0.864). When analyzing a subset of only preoperative variables, the top-performing models were lasso regression (AUC = 0.865) and DNN with SMOTE (AUC = 0.864), both of which outperform any currently published models. Main model contributions relied heavily on variables associated with history of thromboembolic events, length of surgical/anesthetic time, and use of postoperative chemoprophylaxis. Conclusions: The current study provides promise toward machine learning methods geared toward predicting postoperative complications after spine surgery. Further study is needed in order to best quantify and model real-world risk for such events