Significant Difference In Accuracy Research Articles

Direct Comparison of Diagnostic Accuracy of Fast Kilovoltage Switching Dual-Energy Computed Tomography and Magnetic Resonance Imaging for Detection of Enhancement in Renal Masses.

The aim of the study was to compare diagnostic accuracy of dual-energy computed tomography (DECT) and magnetic resonance imaging (MRI) to detect enhancement in renal masses. Adults renal masses of 10 mm or greater with both fast kilovoltage potential switching DECT and contrast-enhanced MRI performed within 12 months were retrospectively included. Two blinded radiologists independently evaluated for enhancement subjectively (5-point Likert scales) and quantitatively (signal intensity ratio ≥15% for MRI, iodine concentration ≥1.2 or ≥2.0 mg/mL for DECT). Per-lesion diagnostic accuracy, with histologic reference standard for solid masses, was expressed as the area under the receiver operator curve (AUC) for each index test. Differences were evaluated for statistical significance using the DeLong test. We included 24 patients with 41 masses: 17 solid renal masses and 24 Bosniak 1 or 2 cysts. There was no significant difference in diagnostic accuracy comparing subjective enhancement by MRI and using iodine overlay DECT for reader 1 (AUC 0.99 vs 0.99, P = 0.38) or reader 2 (AUC 1.00 vs 0.94, P = 0.12) Interobserver agreement was κ = 0.61 for DECT and κ = 0.71 for MRI. There was no significant difference either in accuracy between quantitative assessment using signal intensity ratio or iodine concentration for reader 1 (AUC 0.94 vs 0.94, P = 0.88) or reader 2 (AUC 0.97 vs 0.92, P = 0.16). False-negative results in both subjective and quantitative assessment were nearly exclusively seen in papillary renal cell carcinoma, occurring with both DECT and MRI. We detected no significant differences in accuracy for detecting enhancement in renal masses comparing MRI and DECT. Our results require further investigation in larger sample sizes, but suggest that DECT may be comparable to MRI for detection of enhancement in renal masses.

Machine Learning and Deep Learning CT-Based Models for Predicting the Primary Central Nervous System Lymphoma and Glioma Types: A Multicenter Retrospective Study.

Purpose and BackgroundDistinguishing primary central nervous system lymphoma (PCNSL) and glioma on computed tomography (CT) is an important task since treatment options differ vastly from the two diseases. This study aims to explore various machine learning and deep learning methods based on radiomic features extracted from CT scans and end-to-end convolutional neural network (CNN) model to predict PCNSL and glioma types and compare the performance of different models.MethodsA total of 101 patients from five Chinese medical centers with pathologically confirmed PCNSL and glioma were analyzed retrospectively, including 50 PCNSL and 51 glioma. After manual segmentation of the region of interest (ROI) on CT scans, 293 radiomic features of each patient were extracted. The radiomic features were used as input, and then, we established six machine learning models and one deep learning model and three readers to identify the two types of tumors. We also established a 2D CNN model using raw CT scans as input. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models.ResultsThe cohort was split into a training (70, 70% patients) and validation cohort (31,30% patients) according to the stratified sampling strategy. Among all models, the MLP performed best, with an accuracy of 0.886 and 0.903, sensitivity of 0.914 and 0.867, specificity of 0.857 and 0.937, and AUC of 0.957 and 0.908 in the training and validation cohorts, respectively, which was significantly higher than the three primary physician's diagnoses (ACCs ranged from 0.710 to 0.742, p < 0.001 for all) and comparable with the senior radiologist (ACC 0.839, p = 0.988). Among all the machine learning models, the AUC ranged from 0.605 to 0.821 in the validation cohort. The end-to-end CNN model achieved an AUC of 0.839 and an ACC of 0.840 in the validation cohort, which had no significant difference in accuracy compared to the MLP model (p = 0.472) and the senior radiologist (p = 0.470).ConclusionThe established PCNSL and glioma prediction model based on deep neural network methods from CT scans or radiomic features are feasible and provided high performance, which shows the potential to assist clinical decision-making.

Reappraisal of telesurgery in the era of high-speed, high-bandwidth, secure communications: Evaluation of surgical performance in local and remote environments.

Communication and video transmission delays negatively affect telerobotic surgery. Since latency varies by communication environment and robot, to realize remote surgery, both must perform well. This study aims to examine the feasibility of telerobotic surgery by validating the communication environment and local/remote robot operation, using secure commercial lines and newly developed robots. Hirosaki University and Mutsu General Hospital, 150 km apart, were connected via a Medicaroid surgical robot. Ten surgeons performed a simple task remotely using information encoding and decoding. The required bandwidth, delay time, task completion time, number of errors, and image quality were evaluated. Next, 11 surgeons performed a complex task using gallbladder and intestinal models in local/remote environments; round trip time (RTT), packet loss, time to completion, operator fatigue, operability, and image were observed locally and remotely. Image quality was not so degraded as to affect remote robot operation. Median RTT was 4msec (2-12), and added delay was 29 msec. There was no significant difference in accuracy or number of errors for cholecystectomy, intestinal suturing, completion time, surgeon fatigue, or image evaluation. The fact that remote surgery succeeded equally to local surgery showed that this system has the necessary elemental technology for widespread social implementation.

Digital templating in hip hemiarthroplasty: Is it possible to accurately predict femoral head size from magnification alone?

BackgroundTemplating is an integral part of pre-operative planning in elective hip arthroplasty to achieve favourable long-term outcomes, but its applications in trauma surgery remain limited. When templating from radiographs without a calibration marker, there is always an element of magnification which must be accounted for. Our aim was to establish our institute-specific magnification and to determine whether using this to predict femoral head size in hemiarthroplasty was more accurate than using set magnifications previously reported in the literature. Materials & methodsFifty consecutive patients who underwent hip hemiarthroplasty were retrospectively identified, their pre-operative radiographs reviewed and femoral head measured with templating software. Intra- and inter-observer reliability analyses were performed. Using this value, and two set values of 15% and 21% magnification, we attempted to predict femoral head sizes of our original cohort. The results were compared using paired t-test to ascertain if there was any significant difference in accuracy. ResultsWe established our institute-specific magnification as 17%. Inter- and intra-observer reliability were excellent. However, using this magnification we were only able to correctly predict to within ± one femoral head size in 49% of patients. There was no significant difference in accuracy comparing our institute-specific magnification with other magnifications from the literature. ConclusionWe would not recommend using magnification factor in digital templating software as this could potentially lead to errors in predicting final femoral head size in hip hemiarthroplasty and adversely affect patient outcomes.

Augmented Vision for Dental Students’ Education in Detecting Proximal Carious Lesions on Bitewing Radiographs: A Randomized Controlled Trial

This two-arm, parallel, randomized controlled trial aimed to assess the effect of augmented vision (AV, using interactive color overlays) on the education of dental students in detecting proximal carious lesions on bitewing radiographs compared to black-and-white textbook-like illustrations. Forty-eight preclinical third-year dental students were randomized using a random number generator into two learning groups: test (AV, allowing interaction with color-highlighted carious lesions, n = 24) and control (showing the native radiograph and a black-and-white illustration displaying the carious lesion, n = 24). First, students had 2 weeks to assess 50 bitewings (lesion prevalence on the tooth level: 54.5%) in the test or control. Due to the nature of the intervention, participants could not be blinded toward the intervention. After that, they were asked to detect lesions on 10 independent bitewings and to assess lesion extent (outer/inner enamel; outer/middle/inner dentin). The reference test was constituted by two experienced dentists. No significant differences in accuracy (test 0.84 [95% CI: 0.79, 0.88]; control 0.83 [0.78, 0.87]), AUC (test 0.82 [0.81, 0.84]; control 0.81 [0.80, 0.83]) and F1 score (test 0.79 [0.75, 0.82]; control 0.77 [0.72, 0.81]) were observed between groups. Students of both groups showed difficulties in differentiating enamel from dentin carious lesions. While AV was reported to be motivating by students, it did not increase their accuracy.

A green spectrophotometric method for the simultaneous determination of nasal binary mixture used in respiratory diseases: Applying isosbestic point and chemometric approaches as a resolving tool, greenness evaluation

Nasal drug combination is a very useful therapy for elevating the symptoms of various respiratory diseases as seasonal allergic rhinitis and infectious respiratory illness as pandemic COVID-19. One of best combination is Fluticasone propionate (FLU) and Azelastine (AZE). In this study, different UV spectrophotometric and chemometric methods have been applied for quantitative analysis of FLU and AZE without previous separation in their pure form, laboratory prepared mixture and pharmaceutical dosage form. Absorbance subtraction (AS) and Amplitude modulation (AM) spectrophotometric methods have been applied for the simultaneous determination of the cited drugs. Besides, three well-known chemometric techniques; namely, classical least squares (CLS), partial least square (PLS), and principal component regression (PCR) have been applied for the simultaneous analysis of both drugs by using spectrophotometric data. To be friendly to the environment, the greenness of the proposed methods was taken into consideration and evaluation of the analytical methods’ greenness was done using two green analytical chemistry metrics known as, Analytical Greenness Calculator and an eco-scale scoring method. They indicated that the methods were environmentally friendly in relation to numerous approaches like instrument, reagents, and safety of waste. Analyzing laboratory prepared mixtures including different quantities of FLU and AZE, as well as their marketed dose form, was used to assess the selectivity of the applied methods. The validity of the developed methods was investigated by applying the standard addition technique. The resulting data were statistically compared to those obtained by the official or reported HPLC methods for FLU and AZE, which revealed no significant difference in accuracy and precision at p = 0.05.

Interactions between Ewes and Rams during Mating Can Be Used to Predict Lambing Dates Accurately, but Not Sire.

Simple SummaryThe early prediction of lambing dates for ewes could improve their management at critical time points during pregnancy and throughout lambing. A pilot study used proximity sensors to measure the interactions between ewes and rams during mating to predict conception and lambing dates successfully. This study is an extension of the pilot study using multiple rams under commercial conditions. Lambing dates were predicted based on peak ewe–ram interactions during mating and then compared to the actual lambing dates observed in the paddock. Overall, 91% of lambing dates were predicted within ±6 days and 84% within ±3 days. The use of proximity sensors during mating was an effective means of predicting lambing dates. This could help improve ewe management during pregnancy and lambing and the subsequent survival and performance of their lambs.Ewes often lamb over extended periods so the level of nutrition during pregnancy and lambing may be suboptimal for ewes that conceived later during mating. Predicting lambing dates would allow cohorts of ewes with similar gestational ages to be managed more precisely to achieve targets for ewe nutrition, feed on offer, mob sizes and access to shelter to improve lamb survival. The interactions between ewes and rams during mating have been used to predict the time of oestrus and lambing dates successfully, but this has not been tested at a commercial scale. In this study, proximity sensors were used to measure interactions between inexperienced Merino ewes (n = 317) and experienced rams (n = 9) during a 27-day mating period under commercial production conditions. When the gestation length was assumed to be 150 days, 91% of lambing dates were predicted within ±6 days of the actual birth date of lambs and 84% of lambing dates were predicted within ±3 days. The use of proximity sensors during mating was an effective means of predicting lambing dates, and there was no significant difference in accuracy for single bearing verses multiple bearing ewes. However, DNA parentage data showed the ram corresponding with the maximum daily interactions ratio was the sire for only 16% of all progeny, suggesting they could not be used to indicate the sire of the progeny.

Mechanical Properties and Accuracy Evaluation of 3D Printing Based on Value in the Munsell Color System

Aim. Printing parameters have been studied extensively to improve printing performance, while resins have been studied to a lesser extent. Therefore, we explore the impact of value in the Munsell color system on the mechanical properties and accuracy of printed products. Materials and Methods. Resins having different values were prepared by mixing transparent resin and color pastes. We used plate models for mechanical measurement, crown-and bridge models for accuracy measurement, and disc models for color measurement. Results. The Group E resin, which had the lowest value, had the minimum ultimate flexural strength (61.199 MPa) and a relatively low elastic modulus (19.015 MPa). Excluding the d index, no significant difference in accuracy was observed. In terms of the d index of trueness, the result of the Group E resin was significantly low. The relative errors of trueness were less than 0.0727 μm. The relative errors of precision were less than 0.0093 μm. Conclusion. The value of a resin affects the mechanical properties and accuracy of printed products. It is better to secure flexural strength by using a lower-value resin. High-value resins have relatively low accuracies.

Accuracy of genomic prediction of maternal traits in pigs using Bayesian variable selection methods.

The aim of this study was to compare three methods of genomic prediction: GBLUP, BayesC and BayesGC for genomic prediction of six maternal traits in Landrace sows using a panel of 660 K SNPs. The effects of different priors for the Bayesian methods were also investigated. GBLUP does not take the genetic architecture into account as all SNPs are assumed to have equally sized effects and relies heavily on the relationships between the animals for accurate predictions. Bayesian approaches rely on both fitting SNPs that describe relationships between animals in addition to fitting single SNP effects directly. Both the relationship between the animals and single SNP effects are important for accurate predictions. Maternal traits in sows are often more difficult to record and have lower heritabilities. BayesGC was generally the method with the higher accuracy, although its accuracy was for some traits matched by that of GBLUP and for others by that of BayesC. For piglet mortality within 3 weeks, BayesGC achieved up to 9.2% higher accuracy. For many of the traits, however, the methods did not show significant differences in accuracies.

Accuracy and Safety of Neuronavigation for Minimally Invasive Stabilization in the Thoracolumbar Spine Using Polyaxial Screws-Rod: A Canine Cadaveric Proof of Concept

The main aim of this study was to evaluate the feasibility of minimally invasive stabilization with polyaxial screws-rod using neuronavigation and to assess accuracy and safety of percutaneous drilling of screw corridors using neuronavigation in thoracolumbar spine and compare it between an experienced and a novice surgeon. Feasibility of minimally invasive polyaxial screws-rod fixation using neuronavigation was first performed in the thoracolumbar spine of two dogs. Accuracy and safety of drilling screw corridors percutaneously by two surgeons from T8 to L7 in a large breed dog using neuronavigation were established by comparing entry and exit points coordinates deviations on multiplanar reconstructions between preoperative and postoperative datasets and using a vertebral cortical breach grading scheme. Feasibility of minimally invasive stabilization was demonstrated. For the experienced surgeon, safety was 100% and mean (standard deviation) entry point deviations were 0.3 mm (0.8 mm) lateral, 1.3 mm (0.8 mm) ventral and 0.7 mm (1.8 mm) caudal. The exit points deviations were 0.8 mm (1.9 mm) lateral, 0.02 mm (0.9 mm) dorsal and 0.7 mm (2.0 mm) caudal. Significant difference in accuracy between surgeons was found in the thoracic region but not in the lumbar region. Accuracy and safety improvement are noted for the thoracic region when procedures were repeated by the novice. This proof of concept demonstrates that using neuronavigation, minimally invasive stabilization with polyaxial screws-rod is feasible and safe in a large breed dog model.

In Vitro Comparison of Three Intraoral Scanners for Implant-Supported Dental Prostheses.

With continuing technological developments, there have been advances in the field of fixed prosthetics, particularly in impression-taking techniques. These technological advances mean that a wide variety of diagnostic and/or rehabilitation possibilities can be explored without the need for physical models. The aim of this study was to evaluate the accuracy of three intraoral scanners used in oral implant rehabilitation using an extraoral scanner as a reference and varying the scanning area. Three models representing different clinical scenarios were scanned 15 times by each intraoral scanner and three times by the extraoral scanner. The readings were analyzed and overlaid using engineering software (Geomagic® Control X software (Artec Europe, Luxembourg)). Statistically significant differences in accuracy were found between the three intraoral scanners, iTero® (Align Technology Inc., San Jose, CA, USA), Medit® (Medit®: Seoul, Korea), and Planmeca® (Planmeca®: Helsinki, Finland). In all clinical scenarios, the iTero® scanner had the best trueness (24.4 μm), followed by the Medit® (26.4 μm) and Planmeca® (42.1 μm). The Medit® showed the best precision (18.00 μm) followed by the iTero® (19.20 μm) and Planmeca® (34.30 μm). We concluded that the iTero® scanner had the highest reproducibility and accuracy in the clinical setting.

Statistical evaluation of wind speed forecast models for microgrid distributed control

With the increasing needs to decarbonise existing energy systems, there is an effort to integrate small-scale distributed generation sources, such as wind generators, with the electric demand in circuits known as microgrids. The operation of distributed variable renewable resources is subject to an optimum operating regime, ahead of real-time, which relies on output forecast. However, many wind speed forecast models are designed for centralised controllers, which are vulnerable to control failures. A suitable wind forecast model for a distributed control system is, therefore, required for optimal and reliable use of renewable generation. This paper presents a comparison of wind speed forecast models suited for distributed control, evaluating them in terms of the statistical significant difference in accuracy and computational resource requirements. This is essential since computational resources are limited in distributed control schemes. The data used in this paper is the historical wind speed of the Auchencorth Moss Atmospheric Observatory from 2016 to the end of 2019. Two forecast model types based on Auto-regression and Artificial Neural Network (ANN) are compared using the Diebold-Mariano test. Results show that ANN models with parallel hidden layers have the highest accuracy with statistical significant difference, while remaining suitable for microgrid distributed control.

Area and Feature Guided Regularised Random Forest: a novel method for predictive modelling of binary phenomena. The case of illegal landfill in Canary Island

This paper presents a novel method, Area and Feature Guided Regularised Random Forest (AFGRRF), applied for modelling binary geographic phenomenon (occurrence versus absence). AFGRRF is a wrapper feature-selection method based on a previous modification of Random Forest (RF), namely the Guided Regularised Random Forest (GRRF). AFGRRF produces maps that minimise the affected area without a significant difference in accuracy. For this, it tunes the GRRF hyper-parameters according to a trade of between True Positive Rate and the affected area (Success Rate). AFGRRF also addresses the ‘Rashomon effect’ or the multiplicity of good models. The proposed method was tested to model illegal landfills in Gran Canaria Island (Spain). AFGRRF performance was compared to that of other RF-based methods: (i) standard RF; (ii) Area Random Forest (ARF); (iii) Feature Random Forest (FRF); (iv) Area Feature Random Forest (AFRF) and (v) GRRF. AFGRRF predicted the smallest affected area, 19% of the island, at a similar True Positive Rate. This percentage is substantially smaller than the one predicted by RF (27.43%), ARF (26%), FRF (27.78%), AFRF (23%) and GRRF (29.67%).

Human Stiffness Perception and Learning in Interacting With Compliant Environments.

Humans are capable of adjusting their posture stably when interacting with a compliant surface. Their whole-body motion can be modulated in order to respond to the environment and reach to a stable state. In perceiving an uncertain external force, humans repetitively push it and learn how to produce a stable state. Research in human motor control has led to the hypothesis that the central nervous system integrates an internal model with sensory feedback in order to generate accurate movements. However, how the brain understands external force through exploration movements, and how humans accurately estimate a force from their experience of the force, is yet to be fully understood. To address these questions, we tested human behaviour in different stiffness profiles even though the force at the goal was the same. We generated one linear and two non-linear stiffness profiles, which required the same force at the target but different forces half-way to the target; we then measured the differences in the learning performance at the target and the differences in perception at the half-way point. Human subjects learned the stiffness profile through repetitive movements in reaching the target, and then indicated their estimation of half of the target value (position and force separately). This experimental design enabled us to probe how perception of the force experienced in different profiles affects the participants’ estimations. We observed that the early parts of the learning curves were different for the three stiffness profiles. Secondly, the position estimates were accurate independent of the stiffness profile. The estimation in position was most likely influenced by the external environment rather than the profile itself. Interestingly, although visual information about the target had a large influence, we observed significant differences in accuracy of force estimation according to the stiffness profile.

A systematic review and meta-analysis of implicit Theory of Mind in schizophrenia

IntroductionEveryday social interactions are based on Theory of Mind (ToM) or mentalizing, whose complex processes are involved in understanding, representing one’s own and other people’s mental states. ToM is supposed to have two systems. The implicit ToM seems to be a fast, automatic, non-verbal processing. The explicit ToM is characterized by a slower, but more flexible processing, which is mostly verbal, interpretative. Several studies have described explicit ToM deficit in schizophrenic patients. Less research has investigated implicit ToM in patients, however recently, there has been a growing number of articles examining implicit ToM of patients with schizophrenia.ObjectivesThe aim of our systematic review and meta-analysis is to summarize the results of the implicit ToM in schizophrenia.MethodsA systematic search was performed in four major databases. We included 11 publications. 7 studies; and 5 studies were included the quantitative synthesis and the qualitative synthesis, respectively.ResultsWe found significant differences in accuracy, reaction time and brain activation patterns during implicit ToM between schizophrenic patients and controls. The systematic review revealed further alterations in visual scanning, cue fixation, face looking time, and difficulties in perspective taking.ConclusionsBased on our results implicit ToM is affected in schizophrenia in addition to explicit ToM deficit. However, based on these results we cannot exclude the possibility, that implicit ToM or at least some elements of it might be relatively unaffected (e.g. detection of intentionality), however its effectiveness is limited by non-mentalizing deficits (e.g. certain neurocognitive impairments). Our results may have important implications for the remediation of mentalizing skills.DisclosureThe research is supported by the Hungarian National Excellence Centrum Grant (FIKP II) and Hungarian Brain Research Program (KTIA-13-NAP-A-II/12).

Inter-rater reliability for assessing intracranial collaterals in patients with acute ischemic stroke: comparing 29 raters and an artificial intelligence-based software

PurposeOutcome of endovascular treatment in acute ischemic stroke patients is depending on the collateral circulation maintaining blood flow to the ischemic territory. We evaluated the inter-rater reliability and accuracy of raters and an automated algorithm for assessing the collateral score (CS, range: 0–3) in acute ischemic stroke patients.MethodsBaseline CTA scans with an intracranial anterior occlusion from the MR CLEAN study (n=500) were used. For each core lab CS, ten CTA scans with sufficient quality were randomly selected. After a training session in collateral scoring, all selected CTA scans were individually evaluated for a visual CS by three groups: 7 radiologists, 13 junior and 9 senior radiology residents. Two additional radiologists scored CS to be used as reference, with a third providing a CS to produce a 2 out of 3 consensus CS in case of disagreement. An automated algorithm was also used to compute CS. Inter-rater agreement was reported with intraclass correlation coefficient (ICC). Accuracy of visual and automated CS were calculated.Results39 CTA scans were assessed (1 corrupt CTA-scan excluded). All groups showed a moderate ICC (0.689-0.780) in comparison to the reference standard. Overall human accuracy was 65± 7% and increased to 88± 5% for dichotomized CS (0–1, 2–3). Automated CS accuracy was 62%, and 90% for dichotomized CS. No significant difference in accuracy was found between groups with different levels of expertise.ConclusionAfter training, inter-rater reliability in collateral scoring was not influenced by experience. Automated CS performs similar to residents and radiologists in determining a collateral score.

Diagnostic Performance of Antigen Rapid Diagnostic Tests, Chest Computed Tomography, and Lung Point-of-Care-Ultrasonography for SARS-CoV-2 Compared with RT-PCR Testing: A Systematic Review and Network Meta-Analysis.

(1) Background: The comparative performance of various diagnostic methods for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection remains unclear. This study aimed to investigate the comparison of the 3 index test performances of rapid antigen diagnostic tests (RDTs), chest computed tomography (CT), and lung point-of-care-ultrasonography (US) with reverse transcription-polymerase chain reaction (RT-PCR), the reference standard, to provide more evidence-based data on the appropriate use of these index tests. (2) Methods: We retrieved data from electronic literature searches of PubMed, Cochrane Library, and EMBASE from 1 January 2020, to 1 April 2021. Diagnostic performance was examined using bivariate random-effects diagnostic test accuracy (DTA) and Bayesian network meta-analysis (NMA) models. (3) Results: Of the 3992 studies identified in our search, 118 including 69,445 participants met our selection criteria. Among these, 69 RDT, 38 CT, and 15 US studies in the pairwise meta-analysis were included for DTA with NMA. CT and US had high sensitivity of 0.852 (95% credible interval (CrI), 0.791–0.914) and 0.879 (95% CrI, 0.784–0.973), respectively. RDT had high specificity, 0.978 (95% CrI, 0.960–0.996). In accuracy assessment, RDT and CT had a relatively higher than US. However, there was no significant difference in accuracy between the 3 index tests. (4) Conclusions: This meta-analysis suggests that, compared with the reference standard RT-PCR, the 3 index tests (RDTs, chest CT, and lung US) had similar and complementary performances for diagnosis of SARS-CoV-2 infection. To manage and control COVID-19 effectively, future large-scale prospective studies could be used to obtain an optimal timely diagnostic process that identifies the condition of the patient accurately.

Prediction of visual field defects from macular optical coherence tomography in glaucoma using cluster analysis.

PurposeTo assess the accuracy of cluster analysis‐based models in predicting visual field (VF) defects from macular ganglion cell‐inner plexiform layer (GCIPL) measurements in glaucomatous and healthy cohorts.MethodsGCIPL measurements were extracted from posterior pole optical coherence tomography (OCT), from locations corresponding to central VF test grids. Models incorporating cluster analysis methods and corrections for age and fovea to optic disc tilt were developed from 493 healthy participants, and 5th and 1st percentile limits of GCIPL thickness were derived. These limits were compared with pointwise 5th and 1st percentile limits by calculating sensitivities and specificities in an additional 40 normal and 37 glaucomatous participants, as well as applying receiver operating characteristic (ROC) curve analyses to assess the accuracy of predicting VF results from co‐localised GCIPL measurements.ResultsClustered models demonstrated globally low sensitivity, but high specificity in the glaucoma cohort (0.28–0.53 and 0.77–0.91, respectively), and high specificity in the healthy cohort (0.91–0.98). Clustered models showed similar sensitivities and superior specificities compared with pointwise methods (0.41–0.65 and 0.71–0.98, respectively). There were significant differences in accuracy between clusters, with relatively poor accuracy at peripheral macular locations (p < 0.0001 for all comparisons).ConclusionsCluster analysis‐based models incorporating age correction and holistic consideration of fovea to optic disc tilt demonstrated superior performance in predicting VF results to pointwise methods in both glaucomatous and healthy eyes. However, relatively low sensitivity and poorer performance at the peripheral macula indicate that OCT in isolation may be insufficient to predict visual function across the macula accurately. With modifications to criteria for abnormality, the concepts suggested by the described normative models may guide prioritisation of VF assessment requirements, with the potential to limit excessive VF testing.

Accuracy of noninvasive electrocardiographic imaging using isopotential versus isochronal map for identifying the site of origin of ventricular arrhythmias

Abstract Funding Acknowledgements Type of funding sources: Private hospital(s). Main funding source(s): Learning Health Background and aim Previous studies reporting the results of noninvasive electrocardiographic imaging (ECGI) used both isopotential map and isochronal map to assess the site of origin (SOO) of premature ventricular contractions (PVCs). The aim of this study was to evaluate the accuracy of both methods globally, and according to the location of the arrhythmia: from the right ventricular outflow tract (RVOT) or other locations (non-RVOT). Methods We studied with ECGI 35 consecutive patients with frequent (&amp;gt;10.000/24 h) PVCs. Patients were excluded if the ablation was not performed (7 patients) or was unsuccessful (7 patients). The study group consisted of 21 patients, 11 male, median age of 56 (44-71) years. The ECGI was performed with the epi-endocardial system Amycard. Two noninvasive maps were obtained: isopotential map based on the analysis of the shape and amplitude of the unipolar electrogram (FND method) and an isochronal map obtained using the activation direction method (ADM) (Figure). The invasive activation map was obtained with the Carto or the Ensite system and radiofrequency was applied at the earliest activation site with QS morphology on the unipolar electrogram and a pace match of at least 11/12. The SOO of the PVCs was considered the site where the PVCs were abolished. We assessed the accuracy of the ECGI to identify the SOO of the PVCs using both methods. A perfect match was defined as a predicted location by the ECGI within the same anatomic segment of the actual SOO of the PVCs, whereas a near match as a predicted location within the same segment or a contiguous one. Values are presented as median (Q1-Q3) Results PVCs originated in the RVOT in 11 (52%) patients. The percentage of near matches was not significantly different between FND and ADM methods (95% vs 86%, p=0.50), however the percentage of perfect matches was significantly higher with the FND than with the ADM technique (95% vs 67 %, p=0.031). We found no significant differences in accuracy according to the location of the PVCs, in the RVOT or outside (Table). Conclusions We found a good agreement between ECGI and invasive maps, however the FND technique showed a better accuracy regardless the site of the PVCs.

Effect of shift work on fatigue, reaction time and accuracy of nurses in the Department of Neurology: A cross-sectional observational study.

The purpose of our study was mainly to explore the effect of different shift work on cognitive and executive performance in a real clinical environment among nurses from China. Working in shifts can disrupt circadian rhythm, resulting in reduced sleep duration, which can have a detrimental effect on cognitive function. To provide continuous service for patient care, shift work is often a special requirement for nurses. The Stroop test can be used to measure selective attention and reaction time during executive function. However, there have been limited studies about the effect of shift work on the cognitive performance of nurses by Stroop tests. Additionally, no study has been conducted in nurses working in shifts from China. Registered nurses in general ward, Department of Neurology, from West China Hospital of Sichuan University, were eligible and consecutively included if they were simultaneously responsible for the day, evening and night shifts on the shift work schedule. A fatigue questionnaire and Stroop tests were performed for each subject separately before and after three working shifts (morning, evening and night shift) to measure changes in fatigue, reaction time and accuracy. Eighteen registered nurses (4 males and 14 females) were included in our study, with a median age of 25 years old. The fatigue degree was significantly increased after day and evening shifts (p = .015 and <.001, respectively). Compared with those in the preshifts, the reaction time in the neutral task and incongruent task was significantly quicker after the day shift (p = .001, p < .001) and night shift (p = .008, .019). In contrast, the reaction time after the evening shift was mildly increased, although without significance. There was no significant difference in accuracy among the three different working shifts. In addition, there was a negative correlation between the fatigue score and reaction time on the incongruent task in the Stroop test before the day shift (r = -.542, p = .020). The fatigue score in the postday shift was found to be inversely related to working hours in the daytime (r = -.500, p = .034). Our study showed that increased fatigue was found in nurses after day and evening shifts, and shift work can affect the reaction time after the evening shift. However, there was no significant difference in accuracy and a high level of attention could be maintained among the three working shifts, suggesting a highly developed sense of responsibility in our nurses. In addition to focusing on the common adverse effects of evening or night shifts on nurses, fatigue in the day shift should be paid special attention to by the leadership in the nursing management. Also, nurse managers can implement specific strategies to reduce fatigue after the day shift by shortening the working hours in the daytime appropriately, setting rest periods in the day shift or dividing nurses into morning and afternoon shifts.