中医舌诊术语英译国际标准探究＊

本文通过对《中医基本名词术语中英对照国际标准》和《WHO西太平洋地区传统医学名词术语国际标准》舌诊术语进行比较，分析两部标准中舌诊术语英译的优缺点，提出更适宜优先选择作为中医舌诊术语英译标准的方案，以期为中医名词术语标准化工作提供参考。     

Predefined vs data-guided training prescription based on autonomic nervous system variation: A systematic review

Monitoring variations in the functioning of the autonomic nervous system may help personalize training of runners and provide more pronounced physiological adaptations and performance improvements. We systematically reviewed the scientific literature comparing physiological adaptations and/or improvements in performance following training based on responses of the autonomic nervous system (ie, changes in heart rate variability) and predefined training. PubMed, SPORTDiscus, and Web of Science were searched systematically in July 2019. Keywords related to endurance, running, autonomic nervous system, and training. Studies were included if they (a) involved interventions consisting predominantly of running training; (b) lasted at least 3 weeks; (c) reported pre- and post-intervention assessment of running performance and/or physiological parameters; (d) included an experimental group performing training adjusted continuously on the basis of alterations in HRV and a control group; and (e) involved healthy runners. Five studies involving six interventions and 166 participants fulfilled our inclusion criteria. Four HRV-based interventions reduced the amount of moderate- and/or high-intensity training significantly. In five interventions, improvements in performance parameters (3000 m, 5000 m, Loadmax, Tlim) were more pronounced following HRV-based training. Peak oxygen uptake () and submaximal running parameters (eg, LT1, LT2) improved following both HRV-based and predefined training, with no clear difference in the extent of improvement in . Submaximal running parameters tended to improve more following HRV-based training. Research findings to date have been limited and inconsistent. Both HRV-based and predefined training improve running performance and certain submaximal physiological adaptations, with effects of the former training tending to be greater.     

Improving Surgical Training

Improving Surgical Training is a programme piloting an innovative, evidence-based approach to training. It was developed in response to the Shape of Training report which reviewed postgraduate training and recommended changes in medical education to meet the demands of the modern NHS. A series of initiatives have been developed to enhance the experience for surgical trainees not only to encourage a more focussed and supported method, but also to improve their job satisfaction. The initiatives have combined a greater emphasis on time for training provided by trainers with allocated time for training with multidisciplinary teamworking and the use of technology enhanced learning with simulation of both technical and non-technical skills. The pilot started in 2018 with core training in general surgery and has been expanded to include vascular surgery, urology and trauma and orthopaedics over the last 2 years. Initial feedback from both trainees, trainers and schools of surgery have identified different challenges to aid implementation. The programme is being very carefully evaluated by an independent company as well as careful oversight by the General Medical Council which are paramount to its success.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

镇江市护士分层培训及分层进阶现况分析

目的调查镇江市各医院护士分层培训及分层进阶的方法及现状。方法2018年4—5月间采用自行设计的调查问卷,对全市医院的护理人员进行随机抽样调查。结果调查显示镇江市96.45%的护士受医院分层培训及分层进阶这一模式的管理。其中N1占16.77%,N2占29.5%,N3占41.9%,N4占8.7%,其他占3.11%。有24.53%的护士对医院目前的分层培训方式感到满意并认为无需改进,40.99%的护士表示满意,但需要改进,29.19%的护士表示基本满意,需要改进,5.28%的护士表示不满意需要较大改进。95.82%的医院科室对不用层级的护士有不同的核心能力要求并根据不同核心能力要求进行培训。结论护士分层培训几分层进阶这一管理模式在镇江各医院都有体现,但各医院实行的方式有较大差异,配套的管理方式也有待完善。