Reliability of thoracolumbar burst fracture classification in the Swedish Fracture Register.

BACKGROUND: The Swedish Fracture Register (SFR) is a national quality register for all types of fractures in Sweden. Spine fractures have been included since 2015 and are classified using a modified AOSpine classification. The aim of this study was to determine the accuracy of the classification of thoracolumbar burst fractures in the SFR. METHODS: Assessments of medical images were conducted in 277 consecutive patients with a thoracolumbar burst fracture (T10-L3) identified in the SFR. Two independent reviewers classified the fractures according to the AOSpine classification, with a third reviewer resolving disagreement. The combined results of the reviewers were considered the gold standard. The intra- and inter-rater reliability of the reviewers was determined with Cohen's kappa and percent agreement. The SFR classification was compared with the gold standard using positive predictive values (PPV), Cohen's kappa and percent agreement. RESULTS: The reliability between reviewers was  high (Cohen's kappa 0.70-0.97). The PPV for correctly classifying burst fractures in the SFR was high irrespective of physician experience (76-89%), treatment (82% non-operative, 95% operative) and hospital type (83% county, 95% university). The inter-rater reliability of B-type injuries and the overall SFR classification compared with the gold standard was low (Cohen's kappa 0.16 and 0.17 respectively). CONCLUSIONS: The SFR demonstrates a high PPV for accurately classifying burst fractures, regardless of physician experience, treatment and hospital type. However, the reliability of B-type injuries and overall classification in the SFR was found to be low. Future studies on burst fractures using SFR data where classification is important should include a review of medical images to verify the diagnosis.

Enhanced lung cancer detection: Integrating improved random walker segmentation with artificial neural network and random forest classifier.

Background: Medical image segmentation is a vital yet difficult job because of the multimodality of the acquired images. It is difficult to locate the polluted area before it spreads. Methods: This research makes use of several machine learning tools, including an artificial neural network as well as a random forest classifier, to increase the system's reliability of pulmonary nodule classification. Anisotropic diffusion filtering is initially used to remove noise from a picture. After that, a modified random walk method is used to get the region of interest inside the lung parenchyma. Finally, the features corresponding to the consistency of the picture segments are extracted using texture-based feature extraction for pulmonary nodules. The final stage is to identify and classify the pulmonary nodules using a classifier algorithm. Results: The studies employ cross-validation to demonstrate the validity of the diagnosis framework. In this instance, the proposed method is tested using CT scan information provided by the Lung Image Database Consortium. A random forest classifier showed 99.6 percent accuracy rate for detecting lung cancer, compared to a artificial neural network's 94.8 percent accuracy rate. Conclusions: Due to this, current research is now primarily concerned with identifying lung nodules and classifying them as benign or malignant. The diagnostic potential of machine learning as well as image processing approaches are enormous for the categorization of lung cancer.

Accuracy of two-dimensional digital planning in uncemented primary hip arthroplasty: monocentric analysis of eight hundred implants.

PURPOSE: In the last decades, there has been a refinement in total hip arthroplasty, which allowed surgeons to achieve the highest performance and better patient outcomes. Preoperative planning in primary hip arthroplasty is an essential step that guides the surgeon in restoring the anatomy and biomechanics of the joint. This study aims to evaluate the accuracy of the 2D digital planning, considering cup sizing, stem sizing, and limb length discrepancy. Additionally, we conducted a multivariable analysis of demographic data and comorbidities to find factors influencing preoperative planning. METHODS: This retrospective study analyzed the planning accuracy in 800 consecutive uncemented primary total hip arthroplasty. We compared the preoperatively planned total hip arthroplasty with postoperative results regarding the planned component size, the implanted size, and the lower limb length restoration. Therefore, we investigated factors influencing planning accuracy: overweight and obesity, sex, age, past medical history, comorbidities, and implant design. All the surgeries were performed in the posterolateral approach by one expert surgeon who did the preoperative planning. The preoperative planning was determined to be (a) exact if the planned and the implanted components were the same size and (b) accurate if exact ± one size. The restoration of postoperative limb length discrepancy was classified into three groups: ± 3 mm, ± 5 mm, and ± 10 mm. This assessment was performed through a digital method 2D based on a standard hip X-ray. RESULTS: This court of 800 implants showed that planning was exact in 60% of the cups and 44% of the stems and was accurate in 94% of the cups and 80% of the stems. The postoperative limb length discrepancy was ± 3 mm in 91% and ± 5 mm in 97%. CONCLUSIONS: This study showed preoperative 2D digital planning great precision and reliability, and we demonstrated that it was accurate in 94% of the cups and 80% of the stems. Therefore, the preoperative limb length discrepancy analysis was essential to guarantee the recovery of the operated limb's correct length.

Optimal detection and classification of grid connected system using MSVM-FSO technique.

This paper, a hybrid method, is proposed for protecting the hybrid photovoltaic (PV) and wind turbine (WT) system. The proposed protecting method is the hybrid wrapper of both the multiple support vector machine (MSVM) and firebug swarm optimization (FSO), commonly named as MSVM-FSO method. The proposed technique is diagnosing the appropriate fault occurring in the hybrid system. The main purpose of the proposed system is to assure the system with lower complexity for the fault diagnosis and detection (FDD) for improving the power quality (PQ) of hybrid method. Here, the MSVM approach is used to detect the fault conditions of grid-tied system. To evaluate the events of voltages, fault and the currents of hybrid systems are analyzed at the feeder of buses. The FSO categorizes the types of fault, which is occurred in grid-connected system. By then, the proposed method's performance is done in the MATLAB software and it is contrasted with different existing methods. From this, the proposed method provides accuracy as 99.7% and efficiency as 98%, which is high compared to existing methods.

Impression Disinfection and Its Effect on Dimensional Accuracy and Surface Detail in the Times of COVID-19: An In Vitro Study.

Introduction The disinfection of impressions is crucial to eliminate the viral and other microbial loads to prevent the cross contamination of diseases. The aim of this study was to compare the effect of different virucidal disinfecting methods on the dimensional accuracy and surface detail reproduction (SDR) of impression materials. Methods A total of 160 samples were fabricated with different impression materials using zinc oxide eugenol (Group 1), alginate (Group 2), polyether (Group 3), and addition silicone (Group 4) impression materials, each containing 40 samples (n=40). These groups were further divided into Subgroups A, B, C, and D (n=10) based on the disinfecting method used. Disinfection was carried out using 0.2% peracetic acid (A), a natural polymer of glucosamine (B), ultraviolet (UV) radiation (C), and ozonated water (D). The disinfected impressions were poured in type IV gypsum, and the obtained casts were checked for dimensional accuracy and surface detail reproduction (SDR). For dimensional accuracy, a one-way analysis of variance (ANOVA) test and, for surface detail reproduction, the chi-square test were used to compare the different subgroups of each impression material separately. Results Zinc oxide eugenol samples showed the lowest mean dimensional change when disinfected with 0.2% peracetic acid (1A=154.1 µm), and alginate showed the lowest mean dimensional change when disinfected using ozonated water (2D=134.9 µm). On the other hand, the lowest mean dimensional change observed in polyether and addition silicone samples was those which were disinfected using UV radiation (3C=100.9 µm and 4C=113.5 µm). Surface detail was reproduced adequately in most of the samples. Conclusion A 0.2% peracetic acid could be used to disinfect zinc oxide eugenol impressions, ozonated water for alginate impressions, and UV radiation for polyether and addition silicone impressions.

The role of experimenter familiarity in children's eyewitness identification.

Child eyewitnesses show a high false identification rate on target-absent (TA) lineups despite good performance on target-present (TP) lineups. One explanation is that children feel a social pressure to choose when presented with a TA lineup. We investigated whether experimenter familiarity would reduce social pressure and improve accuracy on TA lineups. Children (5-7 years, N = 120) watched a short video of a staged theft; 1-2 days later they completed a TP or TA lineup with a familiar or unfamiliar experimenter. Experimenter familiarity had an impact on lineup response in TA lineups only, with more correct 'not there' and fewer 'not sure' responses when the children were familiar with the experimenter. The results provide further evidence to support the social aspect of eyewitness identification decisions in children and provide a possible strategy to improve identification accuracy for those working with children in the criminal justice system.

An improvement method for pancreas CT segmentation using superpixel-based active contour.

Pancreas is one of the most challenging organs for CT image automatic segmentation due to its complex shapes and fuzzy edges. It is simple and universal to use the traditional segmentation method as a post-processor of deep learning method for segmentation accuracy improvement. As the most suitable traditional segmentation method for pancreatic segmentation, the active contour model(ACM), still suffers from the problems of weak boundary leakage and slow contour evolution speed. Therefore, a convenient post-processor for any deep learning methods using superpixel-based active contour model(SbACM) is proposed to improve the segmentation accuracy. Firstly, the superpixels with strong adhesion to edges are used to guide the design of narrowband and energy function. A multi-scale evolution strategy is also proposed to reduce the weak boundary leakage and comprehensively improve the evolution speed. Secondly, using the original image and the coarse segmentation results obtained from deep learning methods as inputs, the proposed SbACM method is used as a post-processor for fine segmentation. Finally, the pancreatic segmentation public dataset TCIA from the National Institutes of Health(NIH, USA) is used for evaluation, and the Wilcoxon Test confirmed that the improvement of proposed method is statistically significant. The results show that: (1) The superpixel-based narrowband shape and dynamic edge energy of the proposed SbACM work for boundary leakage reduction, as well as the multi-scale evolution strategy and dynamic narrowband width for the evolution speed improvement; (2)As a post-processor, SbACM can increase the Dice similarity coefficients(DSC) of five typical UNet-based models, including UNet, SS-UNet, PBR UNet, ResDSN, and nnUNet, 2.35% in average and 9.04% in maximum. (3)Based on the best backbone nnUNet, the proposed post-processor performs better than either adding edge awareness or adding edge loss in segmentation enhancement without increasing the complexity and training time of deep learning models.

Accuracy of physical examination versus Doppler ultrasonography for determining maturity in postoperative arteriovenous fistula formation.

BACKGROUND: Differentiating between arteriovenous fistula (AVF) maturation and failure can help determine which AVF will undergo successful canulation and which ones will require immediate rescue. METHODS: A prospective observational study was conducted at Vajira Hospital in Bangkok, Thailand, between October 2020-November 2022. A single vascular surgeon performed a physical examination on patients with chronic kidney disease undergoing AVF placement, and a radiologist conducted Doppler ultrasonography during the second and sixth postoperative weeks. Dialysis nurses determined AVF maturity by performing cannulation. The study compared the accuracy of physical examination and Doppler ultrasonography. A model was developed to predict the success of AVF use. RESULTS: Out Of the 125 recruited patients, 81% demonstrate unassisted maturation of their AVF. The male sex and brachiocephalic type are associated with AVF maturation. The physical examination findings of palpable thrill and the absence of a strong pulse at the 6th week show an area under the receiver operating curve (AUC) value of 0.79. Similarly, arterial end-diastolic velocity on ultrasonography at the 6th week also demonstrates a comparable predictive value with an AUC of 0.82 (p = 0.697). Meanwhile, the model that combined end-diastolic velocity and venous volume flow yields the best results for predicting AVF maturation, with an AUC of 0.92. These models provide more accurate predictions compared to physical examination alone (AUC: 0.92 vs. 0.79; p < 0.01). CONCLUSION: Although a prudent physician predicts AVF maturation through a thorough physical examination, Doppler ultrasonography is preferred in anticipating the success of postoperative AVF placement.

Good outcome prediction after out-of-hospital cardiac arrest: A prospective multicenter observational study in Korea (the KORHN-PRO registry).

AIM: To assess the ability of clinical examination, biomarkers, electrophysiology and brain imaging, individually or in combination to predict good neurological outcomes at 6 months after CA. METHODS: This was a retrospective analysis of the Korean Hypothermia Network Prospective Registry 1.0, which included adult out-of-hospital cardiac arrest (OHCA) patients (≥18 years). Good outcome predictors were defined as both pupillary light reflex (PLR) and corneal reflex (CR) at admission, Glasgow Coma Scale Motor score (GCS-M) >3 at admission, neuron-specific enolase (NSE) <17 µg/L at 24-72 h, a median nerve somatosensory evoked potential (SSEP) N20/P25 amplitude >4 µV, continuous background without discharges on electroencephalogram (EEG), and absence of anoxic injury on brain CT and diffusion-weighted imaging (DWI). RESULTS: A total of 1327 subjects were included in the final analysis, and their median age was 59 years; among them, 412 subjects had a good neurological outcome at 6 months. GCS-M >3 at admission had the highest specificity of 96.7% (95% CI 95.3-97.8), and normal brain DWI had the highest sensitivity of 96.3% (95% CI 92.9-98.4). When the two predictors were combined, the sensitivities tended to decrease (ranging from 2.7-81.1%), and the specificities tended to increase, ranging from81.3-100%. Through the explorative variation of the 2021 European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) prognostication strategy algorithms, good outcomes were predicted, with a specificity of 83.2% and a sensitivity of 83.5% in patients by the algorithm. CONCLUSIONS: Clinical examination, biomarker, electrophysiology, and brain imaging predicted good outcomes at 6 months after CA. When the two predictors were combined, the specificity further improved. With the 2021 ERC/ESICM guidelines, the number of indeterminate patients and the uncertainty of prognostication can be reduced by using a good outcome prediction algorithm.

Accuracy and clinical characteristics of robot-assisted cervical spine surgery: a systematic review and meta-analysis.

PURPOSE: To evaluate the accuracy and feasibility of robot-assisted cervical screw placement and factors that may affect the accuracy. METHODS: A comprehensive search was made on PubMed, Embase, Cochrane Library, Web of Science, CNKI, and Wanfang Med for the selection of potential eligible literature. The outcomes were evaluated in terms of the relative risk (RR) or standardized mean difference (MD) and corresponding 95% confidence interval (CI). Subgroup analyses of the accuracy of screw placement at different cervical segments and with different screw placement approaches were performed. A comparison was made between robotic navigation and conventional freehand cervical screw placement. RESULTS: Six comparative cohort studies and five case series studies with 337 patients and 1342 cervical screws were included in this study. The perfect accuracy was 86% (95% CI, 82-89%) and the clinically acceptable rate was 98% (95% CI, 95-99%) in robot-assisted cervical screw placement. The perfect accuracy of robot-assisted C1 lateral mass screw placement was the highest (96%), followed by C6-7 pedicle screw placement (93%) and C2 pedicle screw placement (86%), and the lowest was C3-5 pedicle screw placement (75%). The open approach had a higher perfect accuracy than the percutaneous/intermuscular approach (91% vs 83%). Compared with conventional freehand cervical screw placement, robot-assisted cervical screw placement had a higher accuracy, a lower incidence of perioperative complications, and less intraoperative blood loss. CONCLUSION: With good collaboration between the operator and the robot, robot-assisted cervical screw placement is accurate and feasible. Robot-assisted cervical screw placement has a promising prospect.

Diagnostic accuracy of International System for Reporting Serous Fluid Cytopathology: A systematic review and meta-analysis in malignancy diagnosis.

This study conducts the first meta-analysis to assess the aggregated risk of malignancy associated with each category of the International System for Reporting Serous Fluid Cytopathology (ISRSFC) for reporting serous effusion cytology, while also evaluating diagnostic accuracy. PubMed/MEDLINE and Embase were systematically searched using the keywords "(pleural, peritoneal, and pericardial effusions) AND (serous effusion cytology) OR (International System for Reporting Serous Fluid Cytopathology)". Articles underwent risk of bias assessment using the QUADAS-2 tool. After excluding inadequate samples, a meta-analysis determined sensitivity and specificity for different cutoff points, including "atypical considered positive," "suspicious of malignancy considered positive," and "malignant considered positive." Summary receiver operating characteristic curves assessed diagnostic accuracy, and the diagnostic odds ratio was pooled. Sixteen retrospective cross-sectional studies, totaling 19,128 cases, were included. Sensitivity and specificity for the "atypical and higher risk categories" considered positive were 77% (95% confidence interval [CI], 68%-84%) and 95% (95% CI, 93%-97%) respectively. For the "suspicious for malignancy and higher risk categories" considered positive, sensitivity and specificity were 57% (95% CI, 49%-65%) and 100% (95% CI, 99%-100%) respectively. Sensitivity and specificity for the "malignant" category considered positive for malignancy were 70% (95% CI, 60%-77%) and 99% (95% CI, 98%-99%), respectively. The pooled area under the curve ranged from 85% to 89.5% for each cutoff. This meta-analysis underscores the ISRSFC's accuracy in reporting serous fluid cytology. It emphasizes the diagnostic importance of the "suspicious" and "malignant" categories in identifying malignancy, and the role of the "benign" category in ruling out malignancy.

GRADE guidance 37: rating imprecision in a body of evidence on test accuracy.

OBJECTIVES: To provide guidance on rating imprecision in a body of evidence assessing the accuracy of a single test. This guide will clarify when Grading of Recommendations Assessment, Development and Evaluation (GRADE) users should consider rating down the certainty of evidence by one or more levels for imprecision in test accuracy. STUDY DESIGN AND SETTING: A project group within the GRADE working group conducted iterative discussions and presentations at GRADE working group meetings to produce this guidance. RESULTS: Before rating the certainty of evidence, GRADE users should define the target of their certainty rating. GRADE recommends setting judgment thresholds defining what they consider a very accurate, accurate, inaccurate, and very inaccurate test. These thresholds should be set after considering consequences of testing and effects on people-important outcomes. GRADE's primary criterion for judging imprecision in test accuracy evidence is considering confidence intervals (i.e., CI approach) of absolute test accuracy results (true and false, positive, and negative results in a cohort of people). Based on the CI approach, when a CI appreciably crosses the predefined judgment threshold(s), one should consider rating down certainty of evidence by one or more levels, depending on the number of thresholds crossed. When the CI does not cross judgment threshold(s), GRADE suggests considering the sample size for an adequately powered test accuracy review (optimal or review information size [optimal information size (OIS)/review information size (RIS)]) in rating imprecision. If the combined sample size of the included studies in the review is smaller than the required OIS/RIS, one should consider rating down by one or more levels for imprecision. CONCLUSION: This paper extends previous GRADE guidance for rating imprecision in single test accuracy systematic reviews and guidelines, with a focus on the circumstances in which one should consider rating down one or more levels for imprecision.

Optimization of anti-loosening bolt based on double thread mechanism: Development of ground rolling die and effect of thread accuracy on loosening resistance.

We have devised innovative anti-loosening bolts with a double thread-mechanism (denoted DTB-IIC) composed of coaxial single and multiple coarse threads. In this study, we first developed a high-speed and high-precision grinding system for dedicated thread rolling dies of the DTB-IIC. Compared to conventional electro-discharge machining dies, ground dies significantly reduced processing time and costs, and achieved more than 10 times higher durability in thread rolling tests. Comparative Junker vibration loosening tests based on an ISO standard were conducted on several types of DTB-IICs. The amount of backlash δ between the inner multiple-thread nut and DTB-IIC bolt has a great effect on the initial drop of the bolt axial preload, and the rolled DTB-IIC with a relatively small δ secured a residual axial load rate of ≥85%, which satisfies the rating-1 of good self-locking behavior. A 3D FEM model was employed to simulate the initial loosening process in the Junker test, and the analytical results agreed well with the experimental ones by adjusting δ appropriately. Both experimental and analytical results indicate that the high loosening resistance of DTB-IIC is due to the synergistic effect of the interference mechanism of two types of nuts and the jammed locking state.

Effectiveness of low dose computed tomography to detect fractures in paediatric suspected physical abuse: a systematic review.

PURPOSE: The skeletal survey X-ray series is the current 'gold standard' when investigating suspected physical abuse (SPA) of children, in addition to a non-contrast computed tomography (CT) brain scan. This systematic literature review synthesised findings of published research to determine if low dose computed tomography (LDCT) could detect subtle fractures and therefore replace the skeletal survey X-ray series in the investigation of SPA in children aged under 3 years. METHODS: Five electronic databases and grey literature were systematically searched from their inception to 28 April 2022. Primary studies were included where the population comprised paediatric patients up to 16 years and LDCT was used to detect fractures associated with SPA. Studies involving imaging investigations of the head, standard dose CT examinations or accidental trauma were excluded. RESULTS: Three studies met the inclusion criteria, all of which were case series. These studies did not report many of the criteria required to compare the accuracy of LDCT to X-ray, i.e. they did not meet the criteria for a diagnostic accuracy test. Therefore, it is difficult to conclude from the case series if LDCT is accurate enough to replace X-rays. CONCLUSION: Due to the gap in current literature, a phantom study and subsequent post-mortem CT study are recommended as the primary investigative methods to assess the ability of low-dose CT to identify the subtle fractures associated with SPA and to calculate how low the achievable CT dose can be.

Accuracy of template-based guided dental implant placement-An in vitro comparison of different manufacturing methods.

OBJECTIVES: This study investigates effects of surgical guide manufacturing process on 3D transfer accuracy of planned dental implant position, using three production methods: additive 3D-printed (3DF), subtractive milled (MF), and analog laboratory fabricated templates (LF). MATERIAL AND METHODS: Implant position for a single-tooth gap (#26) planned digitally. 3DF and MF templates were designed digitally, while LF templates were analogously created. For each manufacturing type, 10 surgical guides were fabricated. Each guide was used for template-guided implant placement in model replicas. For evaluation of implant placement, cone beam computed tomography scans of all implanted models were superimposed, and implant positions were determined. Deviations at implant shoulder/apex were measured, and median and inter-quartile range (IQR) were determined for mesio-distal, oro-facial, coronal apico, 3D spaces, and angles. RESULTS: At implant shoulder, vertical components dominated position deviations (up to 1.04 mm, IQR 0.28 mm for 3DF). Horizontal deviations were much lower (mesio-distally up to 0.38 mm, IQR 0.36 mm (LF)). Implant apex shows similar vertical deviations, while horizontal deviations clearly increased compared to shoulder, especially in mesio-distal direction. Median angular deviations were between 2.1° (IQR 2.0 mm, max. 4.2°) for 3DF and 3.3° (IQR 1.9 mm, max. 5.3°) for MF. No statistical differences were found between manufacturing types (Kruskal-Wallis test, p = .05). CONCLUSIONS: The study showed the method of implant guide fabrication did not affect the accuracy of implant placement within the limits of an in vitro environment. All methods resulted in implant placement which did not exceed the accepted safety deviation envelope (1.5-2.0 mm).

How to solve novel problems: the role of associative learning in problem-solving performance in wild great tits Parus major.

Although problem-solving tasks are frequently used to assess innovative ability, the extent to which problem-solving performance reflects variation in cognitive skills has been rarely formally investigated. Using wild breeding great tits facing a new non-food motivated problem-solving task, we investigated the role of associative learning in finding the solution, compared to multiple other non-cognitive factors. We first examined the role of accuracy (the proportion of contacts made with the opening part of a string-pulling task), neophobia, exploration, activity, age, sex, body condition and participation time on the ability to solve the task. To highlight the effect of associative learning, we then compared accuracy between solvers and non-solvers, before and after the first cue to the solution (i.e., the first time they pulled the string opening the door). We finally compared accuracy over consecutive entrances for solvers. Using 884 observations from 788 great tits tested from 2010 to 2015, we showed that, prior to initial successful entrance, solvers were more accurate and more explorative than non-solvers, and that females were more likely to solve the task than males. The accuracy of solvers, but not of non-solvers, increased significantly after they had the opportunity to associate string pulling with the movement of the door, giving them a first cue to the task solution. The accuracy of solvers also increased over successive entrances. Our results demonstrate that variations in problem-solving performance primarily reflect inherent individual differences in associative learning, and are also to a lesser extent shaped by sex and exploratory behaviour.

Error Analysis of Normal Surface Measurements Based on Multiple Laser Displacement Sensors.

The robotic drilling of assembly holes is a crucial process in aerospace manufacturing, in which measuring the normal of the workpiece surface is a key step to guide the robot to the correct pose and guarantee the perpendicularity of the hole axis. Multiple laser displacement sensors can be used to satisfy the portable and in-site measurement requirements, but there is still a lack of accurate analysis and layout design. In this paper, a simplified parametric method is proposed for multi-sensor normal measurement devices with a symmetrical layout, using three parameters: the sensor number, the laser beam slant angle, and the laser spot distribution radius. A normal measurement error distribution simulation method considering the random sensor errors is proposed. The measurement error distribution laws at different sensor numbers, the laser beam slant angle, and the laser spot distribution radius are revealed as a pyramid-like region. The influential factors on normal measurement accuracy, such as sensor accuracy, quantity and installation position, are analyzed by a simulation and verified experimentally on a five-axis precision machine tool. The results show that increasing the laser beam slant angle and laser spot distribution radius significantly reduces the normal measurement errors. With the laser beam slant angle ≥15° and the laser spot distribution radius ≥19 mm, the normal measurement error falls below 0.05°, ensuring normal accuracy in robotic drilling.

Accuracy, Repeatability, and Reproducibility of a Hand-Held Structured-Light 3D Scanner across Multi-Site Settings in Lower Limb Prosthetics.

The aim of this work was to assess the accuracy, repeatability, and reproducibility of a hand-held, structured-light 3D scanner (EINScan Pro 2X Plus with High Definition Prime Pack, SHINING 3D Tech. Co., Ltd., Hangzhou, China), to support its potential use in multi-site settings on lower limb prosthetics. Four limb models with different shapes were fabricated and scanned with a metrological 3D scanner (EINScan Laser FreeScan 5X, SHINING 3D Tech. Co., Ltd., Hangzhou, China) by a professional operator (OP0). Limb models were then mailed to three sites where two operators (OP1, OP2) scanned them using their own structured-light 3D scanner (same model). OP1 scanned limb models twice (OP1-A, OP1-B). OP0, OP1-A, and OP2 scans were compared for accuracy, OP1-A and OP1-B for repeatability, and OP1-A and OP2 for reproducibility. Among all comparisons, the mean radial error was <0.25 mm, mean angular error was <4°, and root mean square error of the radial distance was <1 mm. Moreover, limits of agreement were <3.5% for perimeters and volumes. By comparing these results with respect to clinically-relevant thresholds and to the literature available on other 3D scanners, we conclude that the EINScan Pro 2X Plus 3D Scanner with High Definition Prime Pack has good accuracy, repeatability, and reproducibility, supporting its use in multi-site settings.

A Novel Methodology for Measuring Ambient Thermal Effects on Machine Tools.

Large machine tools are critically affected by ambient temperature fluctuations, impacting their performance and the quality of machined products. Addressing the challenge of accurately measuring thermal effects on machine structures, this study introduces the Machine Tool Integrated Inverse Multilateration method. This method offers a precise approach for assessing geometric error parameters throughout a machine's working volume, featuring a low level of uncertainty and high speed suitable for effective temperature change monitoring. A significant innovation is found in the capability to automatically realise the volumetric error characterisation of medium- to large-sized machine tools at intervals of 40-60 min with a measurement uncertainty of 10 µm. This enables the detailed study of thermal errors which are generated due to variations in ambient temperature over extended periods. To validate the method, an extensive experimental campaign was conducted on a ZAYER Arion G™ large machine tool using a LEICA AT960™ laser tracker with four wide-angle retro-reflectors under natural workshop conditions. This research identified two key thermal scenarios, quasi-stationary and changing environments, providing valuable insights into how temperature variations influence machine behaviour. This novel method facilitates the optimization of machine tool operations and the improvement of product quality in industrial environments, marking a significant advancement in manufacturing metrology.

Effectiveness of a standardized quality control management procedure for COVID-19 RT-PCR testing: a large-scale diagnostic accuracy study in China.

BACKGROUND: The study aimed to construct a standardized quality control management procedure (QCMP) and access its accuracy in the quality control of COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR). METHODS: Considering the initial RT-PCR results without applying QCMP as the gold standard, a large-scale diagnostic accuracy study including 4,385,925 participants at three COVID-19 RT-PCR testing sites in China, Foshan (as a pilot test), Guangzhou and Shenyang (as validation sites), was conducted from May 21, 2021, to December 15, 2022. RESULTS: In the pilot test, the RT-PCR with QCMP had a high accuracy of 99.18% with 100% specificity, 100% positive predictive value (PPV), and 99.17% negative predictive value (NPV). The rate of retesting was reduced from 1.98% to 1.16%. Its accuracy was then consistently validated in Guangzhou and Shenyang. CONCLUSIONS: The RT-PCR with QCMP showed excellent accuracy in identifying true negative COVID-19 and relieved the labor and time spent on retesting.