Objective: To secure patient safety, skills needed for laparoscopy are preferably obtained in a non-patient setting. Therefore, we assessed face and construct validity of performance of a salpingectomy in case of ectopic pregnancy on the SimSurgery SEP VR simulator. Materials and Methods: Fifteen experienced gynecologists ( ‡ ESGE level 2) and 17 novices (no laparoscopy experience) performed the Place Arrow (PA), Inspect Abdomen (IA), and Ectopic Pregnancy (EP) tasks and evaluated realism and didactic value of the simulator on 5-point scales. Their task performance was assessed according to the time needed to complete the tasks, total instrument path length, and parameters that indicated quality of performance. Results: The experienced gynecologists performed the PA task significantly faster ( p = 0.003, Mann-Whitney U-test) and with a shorter total instrument path length ( p = 0.001) compared to novices. The experienced gynecologists performed the EP task significantly better on parameters that indicate quality of performance, such as amount of blood loss ( p = 0.019), time to react to blood loss ( p = 0.020), and time of suction in the air ( p = 0.007) compared to novices. Between both groups, no significant differences were found at all for the IA task. Data from the questionnaire revealed that, in general, all participants had a favorable opinion toward the EP module on the SimSurgery SEP. Conclusions: This study demonstrates that the SimSurgery SEP simulator offers a realistic representation of the salpingectomy procedural task according to both experienced gynecologists as well as novices (face validity), and that the simulator can discriminate between different levels of expertise (construct validity) for the PA and EP tasks. The simulator is also perceived as an important additional training tool for gynecological residents. ( J GYNECOL SURG XX:1)
Rapid and accurate diagnostics of bacterial infections are necessary for efficient treatment of antibiotic-resistant pathogens. Cultivation-based methods, such as antibiotic susceptibility testing (AST), are slow, resource-demanding, and can fail to produce results before the treatment needs to start. This increases patient risks and antibiotic overprescription. Here, we present a deep-learning method that uses transformers to merge patient data with available AST results to predict antibiotic susceptibilities that have not been measured. The method is combined with conformal prediction (CP) to enable the estimation of uncertainty at the patient-level. After training on three million AST results from thirty European countries, the method made accurate predictions for most antibiotics while controlling the error rates, even when limited diagnostic information was available. We conclude that transformers and CP enables confidence-based decision support for bacterial infections and, thereby, offer new means to meet the growing burden of antibiotic resistance.
Objective: To secure patient safety, skills needed for laparoscopy are preferably obtained in a non-patient setting. Therefore, we assessed face and construct validity of performance of a salpingectomy in case of ectopic pregnancy on the SimSurgery SEP VR simulator. Materials and Methods: Fifteen experienced gynecologists ( ‡ ESGE level 2) and 17 novices (no laparoscopy experience) performed the Place Arrow (PA), Inspect Abdomen (IA), and Ectopic Pregnancy (EP) tasks and evaluated realism and didactic value of the simulator on 5-point scales. Their task performance was assessed according to the time needed to complete the tasks, total instrument path length, and parameters that indicated quality of performance. Results: The experienced gynecologists performed the PA task significantly faster ( p = 0.003, Mann-Whitney U-test) and with a shorter total instrument path length ( p = 0.001) compared to novices. The experienced gynecologists performed the EP task significantly better on parameters that indicate quality of performance, such as amount of blood loss ( p = 0.019), time to react to blood loss ( p = 0.020), and time of suction in the air ( p = 0.007) compared to novices. Between both groups, no significant differences were found at all for the IA task. Data from the questionnaire revealed that, in general, all participants had a favorable opinion toward the EP module on the SimSurgery SEP. Conclusions: This study demonstrates that the SimSurgery SEP simulator offers a realistic representation of the salpingectomy procedural task according to both experienced gynecologists as well as novices (face validity), and that the simulator can discriminate between different levels of expertise (construct validity) for the PA and EP tasks. The simulator is also perceived as an important additional training tool for gynecological residents. ( J GYNECOL SURG XX:1)
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