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The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building and deploying an AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia. The proposed system which consists of classification and segmentation will save about 30%-40% of the detection time for physicians and promote the performance of COVID-19 detection. Specifically, working in an interdisciplinary team of over 30 people with medical and/or AI background, geographically distributed in Beijing and Wuhan, we are able to overcome a series of challenges (e.g. data discrepancy, testing time-effectiveness of model, data security, etc.) in this particular situation and deploy the system in four weeks. In addition, since the proposed AI system provides the priority of each CT image with probability of infection, the physicians can confirm and segregate the infected patients in time. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we are able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases.
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AIM: To explore the application effect of competency-based education (CBE) combined with multi-disciplinary team (MDT) teaching mode in respiratory rehabilitation nursing teaching. BACKGROUND: Respiratory rehabilitation, as an important non drug treatment for chronic respiratory disease, started late in China, with low implementation rate in hospitals and insufficient knowledge of nursing staff. Therefore, it is urgent to cultivate high professional level nurses with strong professional ability. DESIGN: A randomized controlled trial. METHODS: We selected nurses from the respiratory and critical care department at a Grade III, Level A hospital in Beijing, who participated in the respiratory rehabilitation training program between March 2020 and August 2022, as our study participants. Participants were grouped using a random number method. Nurses who participated in the study from March 2020 to May 2021 were set as the control group, and the nurses who participated in the study from June 2021 to August 2022 were set as the test group. The control group used traditional teaching methods, and the test group used the CBE combined MDT teaching model. At the end of the test, the theory, operating skills, satisfaction and core competence were assessed. RESULTS: After training, the theoretical examination score of the test group (93.71 ± 1.94) was higher than that of the control group (92.37 ± 1.92), the operational examination score of the test group (93.11 ± 2.12) was higher than that of the control group (91.61 ± 1.93), the overall teaching satisfaction of the test group (4.45 ± 0.50) was higher than that of the control group (4.13 ± 0.57), and the total score of the core competence of the test group (148.73 ± 7.08) was higher than that of the control group (141.02 ± 6.41). The difference between the two groups was statistically significant (P<0.05). CONCLUSION: CBE combined with MDT teaching mode has a good effect in Pulmonary and Critical Care Medicine (PCCM) respiratory rehabilitation nursing teaching.
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Educação Baseada em Competências , Enfermagem em Reabilitação , Humanos , China , Competência ClínicaRESUMO
OBJECTIVE: To study the clinicopathologic features and prognosis of primary lymphoma of breast. METHODS: Forty cases of primary breast lymphoma, diagnosed according to the 2008 World Health Organization classification of hematopoietic and lymphoid tumors, were retrospectively studied. Immunohistochemistry was performed by SP method. The follow-up data were analyzed. RESULTS: (1) All the patients were females and the median age was 47 years. Unilateral and bilateral breast involvement were noted in 36 and 4 patients, respectively. The number of tumor were 31 cases (77.5%, 31/40) less than 3, and 9 cases (22.5%, 9/40) were 3 and more than 3. According to Ann Arbor staging system, 33 cases (82.5%) were in stage I to II and 7 cases (17.5%) in stage III to IV. The level of LDH in 9 cases (24.3%, 9/37) went up. For ECOG scores, 34 cases (85.0%) were 0 to 1 score and 6 cases (15.0%) were more than 2 scores. With respect to international prognostic index, 83.8% (31/37) were of score 0 to 2 and 16.2% (6/37) were of score 3 and more than 3. The axillary lymph nodes of 21 patients (53.8%, 21/39) were involved by the malignancy. (2) Histologically, 38 cases (95.0%, 38/40) were classified as B-cell lymphoma [including 27 cases (67.5%) of diffuse large B-cell lymphoma, 8 cases (20.0%) of mucosa-associated lymphoid tissue lymphoma, 2 cases of follicular lymphoma and 1 case of lymphoplasmacytic lymphoma]. The remaining cases included one case of peripheral T-cell lymphoma and one case of lymphoblastic lymphoma. Immunohistochemically, expression of CD20+/- CD79a were demonstrated in the 38 cases (95.0%) of B-cell lymphoma. The staining for CK was negative in all cases. In 33 cases, the positive rates of MUM-1, bcl-6 and bcl-2 were 57.6% (19/33), 30.3% (10/33) and 72.7% (24/33), respectively. Three cases were germinal center B cell phenotype and 21 cases were non-germinal center B cell phenotype. (3) Follow-up information was available in 37 patients (92.5%, 37/40). Twenty-three patients (62.2%, 23/37) were still alive and fourteen ones (37.8%, 14/37) died. For the 27 cases with diffuse large B-cell lymphoma, the five-year and disease-free survival rates were 48.0% and 36.0%, respectively. CONCLUSIONS: Primary breast lymphoma is a rare disease entity. Diffuse large B-cell lymphoma is the commonest histologic type and the majority show a non-germinal center B cell phenotype. The level of LDH, number of tumor and international prognostic index are of prognostic significance.