A pilot study of orthopaedic resident self-assessment using a milestones' survey just prior to milestones implementation.

OBJECTIVE: To pilot test if Orthopaedic Surgery residents could self-assess their performance using newly created milestones, as defined by the Accreditation Council on Graduate Medical Education. METHODS: In June 2012, an email was sent to Program Directors and administrative coordinators of the 154 accredited Orthopaedic Surgery Programs, asking them to send their residents a link to an online survey. The survey was adapted from the Orthopaedic Surgery Milestone Project. Completed surveys were aggregated in an anonymous, confidential database. SAS 9.3 was used to perform the analyses. RESULTS: Responses from 71 residents were analyzed. First and second year residents indicated through self-assessment that they had substantially achieved Level 1 and Level 2 milestones. Third year residents reported they had substantially achieved 30/41, and fourth year residents, all Level 3 milestones. Fifth year, graduating residents, reported they had substantially achieved 17 Level 4 milestones, and were extremely close on another 15. No milestone was rated at Level 5, the maximum possible. Earlier in training, Patient Care and Medical Knowledge milestones were rated lower than the milestones reflecting the other four competencies of Practice Based Learning and Improvement, Systems Based Practice, Professionalism, and Interpersonal Communication. The gap was closed by the fourth year. CONCLUSIONS: Residents were able to successfully self-assess using the 41 Orthopaedic Surgery milestones. Respondents' rate improved proficiency over time. Graduating residents report they have substantially, or close to substantially, achieved all Level 4 milestones. Milestone self-assessment may be a useful tool as one component of a program's overall performance assessment strategy.

Machine Learning-based Techniques to Address Spectral Distortions in Photon Counting X-ray Computed Tomography

<p>Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables full spectrum CT in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical eects in the detector and are very noisy due to photon starvation. In this work, we proposed two methods based on machine learning to address the spectral distortion issue and to improve the material decomposition. This rst approach is to model distortions using an articial neural network (ANN) and compensate for the distortion in a statistical reconstruction. The second approach is to directly correct for the distortion in the projections. Both technique can be done as a calibration process where the neural network can be trained using 3D printed phantoms data to learn the distortion model or the correction model of the spectral distortion. This replaces the need for synchrotron measurements required in conventional technique to derive the distortion model parametrically which could be costly and time consuming. The results demonstrate experimental feasibility and potential advantages of ANN-based distortion modeling and correction for more accurate K-edge imaging with a PCXD. Given the computational eciency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.</p>