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11.
《Clinical neurophysiology》2019,130(12):2231-2237
ObjectiveThe clinical and neurophysiological characteristics of myoclonus in Angelman syndrome (AS) have been evaluated in single case or small cohorts, with contrasting results. We evaluated the features of myoclonus in a wide cohort of AS patients.MethodsWe performed polygraphic EEG-EMG recording in 24 patients with genetically confirmed AS and myoclonus. Neurophysiological investigations included jerk-locked back-averaging (JLBA), cortico-muscular coherence (CMC) and generalised partial directed coherence (GPDC). CMC and GPDC analyses were compared to those obtained from 10 healthy controls (HC).ResultsTwenty-four patients (aged 3–35 years, median 20) were evaluated. Sequences of quasi-continuous rhythmic jerks mostly occurred at alpha frequency or just below (mean 8.4 ± 1.4 Hz), without EEG correlate. JLBA did not show any clear transient preceding the jerks. CMC showed bilateral over-threshold CMC in alpha band that was prominent on the contralateral hemisphere in the patient group as compared to HC group. GPDC showed a significantly higher alpha outflow from both hemispheres toward activated muscles in the patient group, and a significantly higher beta outflow from contralateral hemisphere in the HC group.ConclusionsThese neurophysiological findings suggest a subcortical generator of myoclonus in AS.SignificanceMyoclonus in AS has not a cortical origin as previously hypothesised. 相似文献
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目的 探讨剂量组学在预测肺癌根治性放疗患者放射性肺炎发生中的应用潜能。方法 回顾性收集行根治性放疗的314例肺癌患者的临床资料、放疗剂量文件、定位及随访CT图像,根据临床资料及影像学随访资料对放射性肺炎进行分级,提取全肺的剂量组学特征,构建机器学习模型。应用1000次自助抽样法(bootstrap)的最小绝对值收敛和选择算子嵌套逻辑回归(LASSO‐LR)及1000次bootstrap的赤池信息量准则(AIC)向后法筛选与放射性肺炎相关的剂量组学特征,随机按照7∶3划分为训练集及验证集,应用逻辑回归建立预测模型,并应用ROC曲线及校正曲线评价模型的性能。结果 共提取120个剂量组学特征,经LASSO‐LR降维筛选得到12个特征进入“特征池”,再经过AIC向后法筛选,最终筛选出6个剂量组学特征进行模型构建,训练集AUC为0.77(95%CI为0.65~0.87),独立验证集AUC为0.72(95%CI为0.64~0.81)。结论 利用剂量组学建立的预测模型具有预测放射性肺炎发生的潜力,但仍需继续纳入多中心数据及前瞻性数据进一步挖掘剂量组学的应用潜能。 相似文献
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Laurie Lovett Novak Jonathan Wanderer David A. Owens Daniel Fabbri Julian Z. Genkins Thomas A. Lasko 《Applied clinical informatics》2021,12(1):164
Background The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types. Objectives In this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms. Methods We used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions. Results Through a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries. Conclusion This study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed. 相似文献
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《Gait & posture》2022
BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters. 相似文献
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《Clinical oncology (Royal College of Radiologists (Great Britain))》2022,34(2):e97-e103
Modern artificial intelligence techniques have solved some previously intractable problems and produced impressive results in selected medical domains. One of their drawbacks is that they often need very large amounts of data. Pre-existing datasets in the form of national cancer registries, image/genetic depositories and clinical datasets already exist and have been used for research. In theory, the combination of healthcare Big Data with modern, data-hungry artificial intelligence techniques should offer significant opportunities for artificial intelligence development, but this has not yet happened. Here we discuss some of the structural reasons for this, barriers preventing artificial intelligence from making full use of existing datasets, and make suggestions as to enable progress. To do this, we use the framework of the 6Vs of Big Data and the FAIR criteria for data sharing and availability (Findability, Accessibility, Interoperability, and Reuse). We share our experience in navigating these barriers through The Brain Tumour Data Accelerator, a Brain Tumour Charity-supported initiative to integrate fragmented patient data into an enriched dataset. We conclude with some comments as to the limits of such approaches. 相似文献
18.
Julia H. Vermylen Gordon J. Wood Elaine R. Cohen Jeffrey H. Barsuk William C. McGaghie Diane B. Wayne 《Journal of pain and symptom management》2019,57(3):682-687
Introduction
Physician communication impacts patient outcomes. However, communication skills, especially around difficult conversations, remain suboptimal, and there is no clear way to determine the validity of entrustment decisions. The aims of this study were to 1) describe the development of a simulation-based mastery learning (SBML) curriculum for breaking bad news (BBN) conversation skills and 2) set a defensible minimum passing standard (MPS) to ensure uniform skill acquisition among learners.Innovation
An SBML BBN curriculum was developed for fourth-year medical students. An assessment tool was created to evaluate the acquisition of skills involved in a BBN conversation. Pilot testing was completed to confirm improvement in skill acquisition and set the MPS.Outcomes
A BBN assessment tool containing a 15-item checklist and six scaled items was developed. Students' checklist performance improved significantly at post-test compared to baseline (mean 65.33%, SD = 12.09% vs mean 88.67%, SD = 9.45%, P < 0.001). Students were also significantly more likely to have at least a score of 4 (on a five-point scale) for the six scaled questions at post-test. The MPS was set at 80%, requiring a score of 12 items on the checklist and at least 4 of 5 for each scaled item. Using the MPS, 30% of students would require additional training after post-testing.Comments
We developed a SBML curriculum with a comprehensive assessment of BBN skills and a defensible competency standard. Future efforts will expand the mastery model to larger cohorts and assess the impact of rigorous education on patient care outcomes. 相似文献19.
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