飞秒激光微小切口基质透镜取出术基质切削可预测性研究

目的：比较飞秒激光微小切口基质透镜取出术(SMILE)术后角膜基质实际切削量与术前预测量间差别,评估SMILE基质切削的可预测性与精确性。

方法：前瞻性研究。选取2019-01/07在云南大学附属医院行SMILE术近视矫正患者113例220眼,术前,术后1、3mo行常规检查,包括视力、非接触式眼压、等值球镜度(SE)、角膜平均曲率、角膜前表面球面系数、Pentacam眼前节分析。随机选取研究对象中102眼于术前及术后3mo运用A超角膜测厚仪测量角膜中央厚度(CCT)。术后实际切削量为术前角膜最薄点厚度与术后角膜最薄点厚度的差值,误差量为术前预计切削量与术后实际切削量的差值。观察切削误差大小,并对误差量与术前各生理参数进行相关性分析。

结果：SMILE术后效果良好,术后1、3mo角膜形态和视力相对稳定。A超角膜厚度测厚仪测量数据与Pentacam眼前节分析系统中角膜最薄点数据一致性较好。术前预计切削量101.36±18.91μm,术后1、3mo实际切削量88.89±18.69、84.95±18.64μm比较有差异(F=334.65,P<0.01),术后1、3mo时切削误差量为12.59±9.78、16.50±9.21μm,两者间比较有差异(t=-8.370,P<0.01)。术后1、3mo切削误差量均只与术前SE具有相关性(r=0.299,P<0.01; r=0.305,P<0.01)。术后1、3mo SE与同时间点切削误差量具有相关性(r=-0.275,P<0.01; r=-0.306,P<0.01),随着切削量误差增加,术后等值球镜向负镜度偏移。

结论：SMILE术后角膜基质实际切削量小于术前预测切削量,随着术前屈光度增大,预测切削误差亦增大; 随着误差量增大,术后屈光度逐渐呈负向偏移,但此误差在术后早期并未影响目标视力。     

Epilepsy classification using optimized artificial neural network

ABSTRACT

Objectives: An Electroencephalogram (EEG) is the result of co-operative actions performed by brain cells. In other words, it can be defined as the time course of extracellular field potentials that are generated due to the synchronous action of cells. It is widely used for the analysis and diagnosis of several conditions. But this clinical data use to be multi-dimensional, context-dependent, complex, and it causes a concoction of various computing related research challenges. The objective of this study was to develop a computer-aided diagnosis system for epilepsy detection using EEG signals to ease the diagnosis process.

Materials: In this study, EEG datasets for epilepsy disease detection were taken from a public domain (Bonn University, Germany). These EEG recordings contain 100 single-channel EEG signals with maximum duration of 23.6 seconds. This data set was recorded intra-cranially and extra-cranially with the help of a 128-channel amplifier system using a common reference point.

Results: For a unique set of EEG signal features, the Optimized Artificial Neural Network model for classification and validation was developed with optimum neurons in the hidden layer. Results were tested on the basis of accuracy, sensitivity, precision, and specificity for all classes. The proposed Particle Swarm Optimized Artificial Neural Network provided 99.3% accuracy for EEG signal classification.

Discussion: Our results indicate that artificial neural network has efficiency to provide higher accuracy for epilepsy detection if the statistical features are extracted carefully. It is also possible to improve results for real time diagnosis by using optimization technique for error reduction.

Abbreviations: EEG: Electroencephalogram CAD: Computer-Aided Diagnosis ANN: Artificial Neural Network PSO: Particle Swarm Optimization FIR: Finite Impulse Response IIR: Infinite Impulse Response MSE: Mean Square Error.     

Human factors in obstetrics

The importance of human factors is becoming increasingly recognized in the healthcare profession. Lack of situational awareness, poor communication and inadequate leadership compounded by unfamiliar teams in a rapidly deteriorating clinical situation put obstetric patients at particular risk. There is much to be learnt from other high-risk industries including aviation and the military. Increasing awareness and training in human factors and utilization of communication tools (such as SBAR) and prompts (including emergency checklists) can help to promote a safer environment.     

Reliability of levator scapulae index in subjects with and without scapular downward rotation syndrome

ObjectivesThe objective of this study was to introduce levator scapulae (LS) measurement using a caliper and the levator scapulae index (LSI) and to investigate intra- and interrater reliability of the LSI in subjects with and without scapular downward rotation syndrome (SDRS).DesignTwo raters measured LS length twice in 38 subjects (19 with SDRS and 19 without SDRS).Main outcome measuresFor reliability testing, intraclass correlation coefficients (ICCs), standard error of measurement (SEM), and minimal detectable change (MDC) were calculated.ResultsIntrarater reliability analysis resulted with ICCs ranging from 0.94 to 0.98 in subjects with SDRS and 0.96 to 0.98 in subjects without SDRS. These results represented that intrarater reliability in both groups were excellent for measuring LS length with the LSI. Interrater reliability was good (ICC: 0.82) in subjects with SDRS; however, interrater reliability was moderate (ICC: 0.75) in subjects without SDRS. Additionally, SEM and MDC were 0.13% and 0.36% in subjects with SDRS and 0.35% and 0.97% in subjects without SDRS. In subjects with SDRS, low dispersion of the measurement errors and MDC were shown.ConclusionsThis study suggested that the LSI is a reliable method to measure LS length and is more reliable for subjects with SDRS.     

Counting Your Chickens before They're Hatched: Power Analysis

How does an investigator know that he has enough subjects in his study design to have the predicted outcomes appear statistically significant? In this Investigators’ Corner I discuss why such planning is necessary, give an intuitive introduction to the calculations needed to determine required sample sizes, and hint at some of the more technical difficulties inherent in this aspect of study planning.     

Analysis of medical malpractice claims to improve quality of care: Cautionary remarks

Medical malpractice claims can be analysed to gain insights aimed at improving quality of care. However, using medical malpractice claims in medical research raises epistemological and methodological concerns related to certain features of the litigation process. Medical research should therefore approach medical malpractice claims with caution. Taking one recent study as a an example, this article insists on three areas of concern: (a) the quantity of legal materials available for analysis; (b) the content of the legal materials available for analysis; and (c) the ways in which the content of the legal materials should be analysed and the types of inferences that it can support. The article concludes with general recommendations for future medical research that would incorporate medical malpractice claims. These recommendations centre around recognizing the qualitative dimension of legal reasoning.