Mean Test Time Research Articles

Comparison of Elisar-Fast and Sita-Fast Strategy for Visual Field Assessment in Glaucoma.

To compare two fast threshold strategies of visual field assessment: SITA-Fast (SF; Humphrey field analyser) and Elisar-Fast (EF; Advanced vision analyser) in patients with glaucoma. In this cross-sectional observational study, of total 192 subjects, 138 subjects [150 eyes, 80 glaucoma subjects (91 eyes) and 58 healthy controls (59 eyes)] were analysed and included. Each subject underwent 24-2 EF and SF in randomized order with a minimum time interval of 1 hour between tests. Mean test-time, pointwise and sectoral sensitivity, significance of values of mean sensitivity (MS) and global indices [mean deviation (MD) & pattern standard deviation (PSD)] and their correlation. The mean test-time was 2.59±0.25 and 3.38±0.28 minutes (P = 0.001) with SF and EF respectively. Correlation coefficient for pointwise threshold values correlated strongly for both the devices (range from 0.70 to 0.92). The Intra class correlation (ICC) value of ≥ 0.8 was observed across all sectors indicating good reliability. Bland-Altman plot denoted 95% of data for MS values within limit of Agreement (LOA). The ICC values for overall MS, MD and PSD were 0.916, 0.913 and 0.872 respectively indicating good reliability. High degree of correlation was observed for MD (r = 0.912, P=0.00) and PSD values (r=0.732, P=0.00). Comparison of values indicated a difference of 1.09dB for MD and 0.06dB for PSD between both strategies. High degree of correlation existed between the global indices and pointwise threshold values. The study documents the ability of EF to successfully assess visual field in patients with glaucoma.

In-Depth Retinal Sensitivity Assessment With the MP3 Type S Microperimeter: A Methods Study.

Retinal sensitivity is frequently listed as an end point in clinical trials, often with long working practices. The purpose of this methods study was to provide a new workflow and reduced test time for in-depth characterization of retinal sensitivity. A workflow for the MP3-S microperimeter with detailed functional characterization of the retina under photopic, mesopic, and scotopic conditions was evaluated. Grids of 32 and 28 test positions for photopic/mesopic and scotopic, respectively, were tested in 12 healthy individuals and compared with an established 68-point grid for test time, mean sensitivity (MS), and bivariate contour ellipse area (BCEA). The mean test time (range; ±SD) was 10.5 minutes (8.4-14.9; ±2.0) in the 68-point grid and 4.3 minutes (3.8-5.0; ±0.4) in the 32-point grid, which was significantly different (P < 0.0001). The mean of difference in test time (±SD; 95% confidence interval) was 6.1 minutes (±2.0; 4.6-7.6). MS and BCEA were significantly correlated between grids (r = 0.89 and 0.74; P = 0.0005 and 0.014, respectively). Mean test time of subjects who underwent the full protocol (n = 4) was 2.15 hours. The protocol suggested herein appears highly feasible with in-depth characterization of retinal function under different testing conditions and in a short test time. The protocol described herein allows for characterization of the retina under different testing conditions and in a short test time, which is relevant due to its potential for patient prognostication and follow-up in clinical settings and also given its increasing role as a clinical trial end point.

IPad-Based Neurocognitive Testing (ImPACT-QT) in Acute Adult Mild Traumatic Brain Injury/Concussion: Study on Practicality and Bedside Cognitive Scores in a Level-1 Trauma Center.

There lacks rapid standardized bedside testing to screen cognitive deficits following mild traumatic brain injury (mTBI). Immediate Post-Concussion Assessment & Cognitive Testing-Quick Test (ImPACT-QT) is an abbreviated-iPad form of computerized cognitive testing. The aim of this study is to test ImPACT-QT utility in inpatient settings. We hypothesize ImPACT-QT is feasible in the acute trauma setting. Trauma patients ages 12-70 were administered ImPACT-QT (09/2022-09/2023). Encephalopathic/medically unstable patients were excluded. Mild traumatic brain injury was defined as documented-head trauma with loss-of-consciousness <30 minutes and arrival Glasgow Coma Scale 13-15. Patients answered Likert-scale surveys. Bivariate analyses compared demographics, attention, motor speed, and memory scores between mTBI and non-TBI controls. Multivariable logistic regression assessed memory score as a predictor of mTBI diagnosis. Of 233 patients evaluated (36 years [IQR 23-50], 71% [166/233] female), 179 (76%) were mTBI patients. For all patients, mean test-time was 9.3 ± 2 minutes with 93% (73/76) finding the test "easy to understand." Mild traumatic brain injury patients than non-TBI control had lower memory scores (25 [IQR 7-100] vs 43 [26-100], P = .001) while attention (5 [1-23] vs 11 [1-32]) and motor score (14 [3-28] vs 13 [4-32]) showed no significant differences. Multivariable-regression (adjustment: age, sex, race, education level, ISS, and time to test) demonstrated memory score predicted mTBI positive status (OR .96, CI .94-.98, P = .004). Immediate Post-Concussion Assessment & Cognitive Testing-Quick Test is feasible in trauma patients. Preliminary findings suggest acute mTBIs have lower memory but not attention/motor scores vs non-TBI trauma controls.

PsyCog: A computerised mini battery for assessing cognition in psychosis

Despite the functional impact of cognitive deficit in people with psychosis, objective cognitive assessment is not typically part of routine clinical care. This is partly due to the length of traditional assessments and the need for a highly trained administrator. Brief, automated computerised assessments could help to address this issue. We present data from an evaluation of PsyCog, a computerised, non-verbal, mini battery of cognitive tests. Healthy Control (HC) (N = 135), Clinical High Risk (CHR) (N = 233), and First Episode Psychosis (FEP) (N = 301) participants from a multi-centre prospective study were assessed at baseline, 6 months, and 12 months. PsyCog was used to assess cognitive performance at baseline and at up to two follow-up timepoints. Mean total testing time was 35.95 min (SD = 2.87). Relative to HCs, effect sizes of performance impairments were medium to large in FEP patients (composite score G = 1.21, subtest range = 0.52–0.88) and small to medium in CHR patients (composite score G = 0.59, subtest range = 0.18–0.49). Site effects were minimal, and test-retest reliability of the PsyCog composite was good (ICC = 0.82–0.89), though some practice effects and differences in data completion between groups were found. The present implementation of PsyCog shows it to be a useful tool for assessing cognitive function in people with psychosis. Computerised cognitive assessments have the potential to facilitate the evaluation of cognition in psychosis in both research and in clinical care, though caution should still be taken in terms of implementation and study design.

Comparison of HOTV optotypes and Lea Symbols: a systematic review and meta-analysis

PurposeTo report the findings of our systematic review and meta-analysis comparing the completion time and testing timeof visual acuity testing using Lea Symbols or HOTV optotypes. MethodsA systematic search of PubMed, SCOPUS, and CINAHL was conducted according to the PRISMA guidelines. ResultsThe completion rate for HOTV optotypes and Lea Symbols visual acuity testing was reported for 7,948 patients (average age, 3.59 years; age range, 2-17; 49.96% females). The 3-year-olds’ completion rate of HOTV was 74.09% (47.93%-93.29%), compared with a Lea Symbols completion rate of 77.80% (53.93%-94.57%). The 4-year-olds’ completion rate was 88.20% (63.48%-99.73%) for HOTV and 90.118% (67.42%-99.90%) for Lea Symbols. In 3-year-olds, the mean testing time was 118.33 ± 6.54 seconds for HOTV and 120.33 ± 6.53 seconds for Lea Symbols (P < 0.0001). The difference in testing times in the 4-year-old age group was also statistically significant (86.98 ± 6.12 seconds for HOTV and 94.32 ± 6.57 seconds for Lea symbols) (P < 0.0001). ConclusionsThere was a statistically significant difference in the testing times between the HOTV optotypes and Lea Symbols in both the 3- and 4-year-old age groups; however, the difference of 2-7 seconds on average is unlikely to be clinically significant,. Testing speed and completion rate improve with age for both tests.

Development and Validation of a Novel Mobile Application for Bone Age Assessment

Background: Despite its pivotal role, the use of bone age is limited due to a lack of physician expertise, time, and access. Objective: To develop and validate a mobile application-based bone age assessment tool. Study Design: The study involved the selection of standardized images (from 307 radiographs), delineation (90 radiographs), evaluation (200 radiographs) and incorporation of regions of interest on the application, and validation against the Tanner-Whitehouse 3 method by an expert (252 radiographs) and non-expert users (110 radiographs). Results: The application-based assessment by expert users had an absolute standardized difference of 4.7 months (95% confidence interval; 4.2-5.2 months), a relative standardized difference deviation of 4.4% (3.9-4.9%), and similar intraindividual [2.8 (1.9-3.7) months versus 3.6 (2.8-4.5) months] and interindividual variation [4.2 (3.3-5.0) versus 4.2 (3.3-5.2) months] compared to the TW3 method. Non-expert user assessment had an absolute standardized difference of 6.7 months (5.6-7.7 months) and a relative standardized difference deviation of 5.8% (4.7-6.9%). The mean test time was lower for application than TW3 for both expert [1.3 (0.2) versus 3.1 (1.0) minutes, p&lt;0.001] and non-expert users [2.8 (1.0) versus 5.0 (1.4) minutes, p&lt;0.001]. Discussion: Our study confirms the accuracy of mobile application-based bone age assessment. This, along with good precision, reduced complexity, and lower time requirement, suggests a potential for its widespread implementation.

Frailty and mortality: Utility of Frail-VIG index in ED short-stay units for older adults

BackgroundFrailty assessment allows the identification of patients at risk of death. The aim here was to study the ability of Frail-VIG Index (FI-VIG) in order to discriminate frailty groups of older adults and garner its correlation with mortality in an Emergency-Department Short-Stay Unit (ED-SSU). MethodsOur observational, single-center, prospective study consecutively included patients over 65-years-old admitted between March 1, 2021, and April 30, 2021. Results302 patients were included (56 % women), mean age 83 ± 8 years, and 39.1 % of them had a functional disability whilst 16.5 % of them had dementia. A total of 174 patients (58 %) met the frailty criteria (FI-VIG ≥ 0.2): 111 (63.8 %) had mild frailty (FI-VIG 0.2–0.36), 52 (29.9 %) had moderate frailty (FI-VIG 0.36–0.55), and 11 (6.3 %) had advanced frailty (FI-VIG > 0.55). Mortality at 30 days, 6 months, and 1 year was analyzed: no frailty was 6.3 %, 10.8 %, and 12.5 %, respectively; mild frailty was 10.8 %, 22.5 %, and 22.5 %, respectively; moderate frailty was 25 %, 34.6 %, and 42.3 %, respectively; advanced frailty was 36.4 %, 54.5 %, and 3.6 %, respectively. This shows the significant differences between the groups (1-year mortality p < 0.001). Mild frailty vs. non-frail HR was 2.47 (95 %CI 1.12–5.46), moderate frailty vs. non-frail HR was 6.93 (95 %CI 3.16–15.23), and advanced frailty vs. non-frail HR was 11.29 (95 %CI 3.54–36.03). The mean test time was 7 min. ConclusionsThere was a strong correlation between frailty degree and mortality at 1, 6, and 12 months. FI-VIG is fast and easy-to-use in this setting. It is routine implementation in ED-SSUs could enable early risk stratification.

Screening for silent aspiration in hyperacute stroke: A feasibility study of clinical swallowing examination and cough reflex testing.

Silent aspiration (SA) is common post-stroke and associated with increased risk of pneumonia, length of stay and healthcare costs. Clinical swallow examinations (CSEs) are unreliable measures of SA. There is no consensus on the clinical components that best detect SA. Cough reflex testing (CRT) is an alternative/adjunct whose SA detection accuracy also lacks consensus. To investigate the feasibility of CSE versus CRT against gold standard flexible endoscopic evaluation of swallowing (FEES) for SA identification and to estimate its prevalence in a hyperacute stroke setting. A single-arm preliminary, prospective, feasibility study of patients less than 72 h post-stroke, over a 31-day period on a hyperacute stroke unit: the Royal Victoria Infirmary, Newcastle-upon-Tyne, UK. Ethical approval for the study was obtained. The study tested the feasibility and acceptability of introducing CRT and developing a standardized CSE. Consent/assent was obtained for all participants. Patients unfit for study were excluded. A total of 62% of patients less than 72 h post-stroke (n = 61) were eligible. A total of 75% of those approached (n = 30) consented. A total of 23 patients completed all tests. The principal barrier was anxiety regarding FEES. Mean test time for CRT = 6 min; CSE = 8 min; FEES = 17 min. Patients rated CRT and FEES on average as moderately uncomfortable. A total of 30% (n = 7) of participants who received FEES presented with SA. CRT, CSE and FEES are feasible in 58% of hyperacute stroke patients in this setting. FEES anxiety is the main recruitment barrier and is not always well tolerated. Results support further work to establish optimum methods and differential sensitivity/specificity of CRT and CSE in hyperacute stroke for SA identification. What is already known on this subject SA significantly increases the risk of pneumonia in the early days post-stroke. CSEs are unreliable for identification of SA risk in this population. CRT is gaining popularity as a potential tool to identify stroke patients at risk of SA, though there are questions regarding the efficacy of the clinical protocol currently being used in the UK. What this study adds to existing knowledge This study demonstrates that it is practical and feasible to carry out a larger scale study in this setting to compare CSE and CRT including a consideration of an approach combining both methods for clinical identification of SA versus FEES. Preliminary findings suggest that CSE may have higher levels of sensitivity than CRT for SA identification. What are the potential or actual clinical implications of this work? The results of this study suggest that further work is needed to establish the optimum methods and differential sensitivity/specificity of clinical tools for SA detection in hyperacute stroke.

Road Pothole Detection Using Unmanned Aerial Vehicle Imagery and Deep Learning Technique

Potholes are considered the main factor for road defects, which leads to road status deterioration, which, consequently will lead to increased road accidents. The first step in road maintenance is to inspect the road surface and then accurately detect potholes. However, manually identifying them is costly and time-consuming. In this study, unmanned aerial vehicle (UAV) imagery was used to create orthophotos of the roads and, using deep learning methods, potholes were detected. The used deep learning method in this study is the "you only look once" (YOLO) algorithm. YOLO is one of the "deep learning-based approaches" to detecting objects and is a single-stage network which requires only one forward propagation across the neural network and focuses on the entire image. The fourth version of YOLO is YOLOV4, which has two different architectures (YOLOv4 and YOLOv4-tiny). Two roads were chosen as the study areas, and to generate the orthophotos of the roads, UAV was used to acquire images. To train both methods in the process of detecting potholes using deep learning, 5300 images were used, 90% used for training and 10% applied for testing. The two used architectures were trained for 6000 iterations. Both methods were evaluated based on the average loss, mean average precision (mAP), and training and testing time. The results showed that the (mAP) values for YOLOv4 and YOLOv4-tiny were 91. 2% and 85.7%, respectively. At the end of the 6000 iterations, the average loss for YOLOv4 is 0.30% and for YOLOv4-tiny is 0.34%. In the training process, YOLOv4 needs 29 seconds for each iteration, while YOLOv4-tiny requires only 8 seconds. In the test process, YOLOv4-tiny is faster at detecting potholes than YOLOv4. The approaches were tested on orthophotos created by processing UAV photos. When comparing the detection of both architectures with visual detection, the results showed that YOLOv4 was able to detect most of the potholes on roads, but YOLOv4-tiny detected a lower number of potholes.

Comparison of the visual field test of Glaufield Lite with Humphrey Field Analyser.

To compare visual field test results of Glaufield Lite AP901 CTS 133 (Appasamy Associates, Mannadipet Commune, Thirubhuvanai, Puducherry, India, hereafter Glaufield Lite) with Humphrey Field Analyser (HFA, Carl Zeiss Meditec, Dublin, California, USA, hereafter HFA). A pilot study at a tertiary eye centre involving 23 normal and 24 glaucoma patients who underwent two consecutive visual field tests on (i) HFA 24-2 SITA Fast and (ii) Glaufield Lite Quick Central program. The mean testing time on HFA was significantly shorter than Glaufield Lite (normals: HFA: 2.75 ± 0.49min, Glaufield Lite: 6.85 ± 0.86min, p < 0.001; glaucoma patients: HFA: 3.45 ± 1.08min, Glaufield Lite: 6.95 ± 0.54min, p < 0.001). Reliability criteria were similar, but false-positivity was lower with Glaufield Lite. Bland-Altman analysis showed poor agreement for mean deviation (MD), [~ 2.69 units less for HFA], and acceptable agreement for pattern standard deviation (PSD) [~ 0.426 units more for HFA] between the two devices. Both perimetric techniques showed reliable test results though test duration was longer with Glaufield Lite perimetry. The MD showed poor agreement, likely due to different scales and principles used for perimetry. The PSD showed acceptable agreement, making it valid for use in glaucoma, though a direct comparison of fields from the two devices is not possible. We recommend using the same perimetry device for follow-up evaluation.

Computational Intelligence in Security of Digital Twins Big Graphic Data in Cyber-physical Systems of Smart Cities

This investigation focuses on the application of computational intelligence to the security of Digital Twins (DTs) graphic data of the Cyber-physical System (CPS). The intricate and diverse physical space of CPS in the smart city is mapped in virtual space to construct the DTs CPS in the smart city. Besides, Differential Privacy Frequent Subgraph-Big Multigraph (DPFS-BM) is employed to ensure data privacy security. Moreover, the analysis and prediction model for the DTs big graphic data (BGD) in the CPS is built based on Differential Privacy-AlexNet (DP-AlexNet). Alexnet successfully solves the gradient dispersion problem of the Sigmoid function of deep network structures. Finally, the comparative analysis approach is utilized to verify the performance of the model reported here by comparing it with Long Short-Term Memory, Convolutional Neural Network, Recurrent Neural Network, original AlexNet, and Multi-Layer Perceptron in a simulation experiment. Through the comparison in the root mean square error, the mean absolute error, the mean absolute percentage error, training time, and test time, the model proposed here outperforms other models regarding errors, time delay, and time consumption. In the same environment, the system performs better with multi-hop paths, extra relays, and a high fading index; in that case, the outage probability is minimal. Therefore, the DP-AlexNet model is suitable for processing BGD. Moreover, its speed acceleration is more apparent than that of other models, with a higher SpeedUp indicator. The research effectively combines data mining and data security, which is of significant value for optimizing the privacy protection technology of frequent subgraph mining on a single multi-graph. Besides, the constructed DTs of CPS can provide excellent accuracy and a prominent acceleration effect on the premise of low errors. In addition, the model reported here can provide reference for the intelligent and digital development of smart cities.

VLC localization: deep learning models by Kalman filter algorithm combined with RSS

In this paper, a new framework is presented for indoor visible light communication (VLC) system, based on Yolo v3, EfficientNetB3, and DenseNet121 deep learning (DL) models, as well as an optimization strategy. The proposed framework consists of two steps: data collecting and DL model training. To start, data is acquired using MATLAB and Kalman Filtering (KF) with averaging approaches. Second, the received signal strength (RSS) is employed as the DL models input, with the Cartesian coordinates as the DL models output. The averaging RSS approach combined with KF algorithm are used in the suggested framework. This work introduces the impacts of Non-Line-of-Sight (NLoS) for initial reflection and Line-of-Sight (LoS) based on the three mentioned DL models. Furthermore, we used Bayesian optimization and automatic hyper-parameter (HP) optimization to increase system efficiency and to reduce positioning error in DL models. The obtained results show that the models outperform existing the HP-RSS-KF-LoS-DL models in terms of localization error when compared to traditional RSS signal-based localization techniques. Many performance indicators are considered to evaluate the proposed framework resiliency, including accuracy (ACC), area under the curve (AUC), sensitivity (Se), and precision (Pr), as well as F1-score, root mean square error (RMSE), training, and testing time. The DL models are generated and trained using Python software on a Kaggle Notebook GPU cloud (2 CPU cores and 13 GB RAM). The achieved results are: 99.99% ACC, 99.98% AUC, 98.88% Se, 98.98% Pr, 99.97% F1-score, 0.112 cm RMSE, and 0.29 s testing time. The proposed system could be easily deployed for autonomous applications, based on the analysis of the experimental data. Several applications can be used depending on enhancing the localization of VLC system in military systems, underwater systems, and indoor systems like hospitals, hotels, libraries and malls.

Identifying Fagaceae and Lauraceae species using leaf images and convolutional neural networks

Lauraceae and Fagaceae are two large woody plant families that are predominant in the low- and middle-altitude regions in Taiwan. The highly interspecific similarity between some species of the family brings limitations on the management and utilization. This work proposed an approach for identifying 15 Lauraceae species and 20 Fagaceae species using leaf images and convolutional neural networks (CNNs). Leaf specimens of 35 species were collected from the northern, central, and southern parts of Taiwan. Images of the leaves were acquired using flat-bed scanners. Three CNN architectures—DenseNet-121, MobileNet V2, and Xception—were trained. Xception achieved the highest mean test accuracy of 99.39%, and MobileNet V2 required the shortest mean test time of 17.1 ms per image using a GPU. The saliency maps revealed that the characteristics learned by models matched the leaf features used by botanists. A pruning algorithm, gate decorator, was applied to the trained models for reducing the number of parameters and number of floating-point operations of the MobileNet V2 by 55.4% and 69.1%, respectively, while the model accuracy was maintained at 92.03%. Thus, MobileNet V2 has the potential to be used for identifying the Lauraceae and Fagaceae species on mobile devices.

Evaluation of a new digital visual acuity chart for best‐corrected visual acuity estimation

AbstractPurposeTo compare estimated best‐corrected visual acuity (BCVA) between the digital chart named AxAnIvIs and the ETDRS chart at different level of acuity, and to determine if there is difference of the test time between the two systems.MethodsPatients at the eye clinic of Uppsala university hospital that met the inclusion criteria (age &gt;55 years, BCVA [1.0; −0.1] and no ocular disease strongly considered to cause visual acuity change within one month) were asked to participate this study. The AxAnIvIs system uses a custom made algorithm for optotypes visualisation where optotypes decrease in size horizontally in one line. Altogether 16 subjects (8 male, 8 female) were included and distributed equally to four visual acuity classes ([1.0 0.8], [0.7 0.5], [0.4 0.2], [0.1 −0.1] logMAR). Each subject’s BCVA was measured and the test time was recorded with the two systems on two separate occasions.ResultsThe mean BCVA difference between the AxAnIvIs system and the ETDRS chart at visual acuity class of [1.0 0.8], [0.7 0.5], [0.4 0.2], [0.1 −0.1] logMAR was estimated as a CI(95%), 0.02 ± 0.04, 0.02 ± 0.08, ‐0.01 ± 0.05, 0.03 ± 0.07 logMAR, respectively. ANOVA shows that there is no significant difference between the two systems and no significant difference between the two systems at the different visual acuity classes. The mean test time between the AxAnIvIs system and the ETDRS chart was estimated as a CI(95%), −37 ± 15 s.ConclusionsThe AxAnIvIs system can be used as a replacement of the ETDRS chart for general examination. The test time with the AxAnIvIs system is half minute shorter than that with the ETDRS chart.

COVID-19 Drive-Through Point of Screening and Testing (POST) System: A Safe, Efficient, and Adaptable Model for Nasopharyngeal Swab Collection.

The authors aim to demonstrate that the current drive-through testing model at a health district was improved in certain parameters compared with a previous testing protocol, and to provide the methodology of the current model for other coronavirus disease (COVID-19) testing sites to potentially emulate. Initially, a small drive-through site was constructed at a converted tuberculosis clinic, but due to an increase in testing needs, an expanded point of screening and testing (POST) system was developed in an event center parking lot to administer tests to a higher volume of patients. An average of 51.1 patients was tested each day (2.0 tests per personnel in personal protective equipment [PPE] per hour) at the initial tuberculosis clinic drive-through site, which increased to 217.8 patients tested each day (5.9 tests per personnel in PPE per hour) with the new drive-through POST system (P < 0.001). Mean testing time was 3.4 minutes and the total time on-site averaged 14.4 minutes. This POST drive-through system serves as an efficient, safe, and adaptable model for high volume COVID-19 nasopharyngeal swabbing that the authors recommend other COVID-19 testing sites nationwide consider adopting for their own use.

Protocol for assessment of the pupillary light reflex in dogs without chemical restraint: preliminary investigation.

To explore the use of a modified pupillometry technique in dogs without chemical restraint. Following dark adaptation, pupillary light reflexes were assessed in six dogs with sudden acquired retinal degeneration syndrome, in the unaffected eye of eight dogs with unilateral primary glaucoma ("predisposed"), and in 11 healthy dogs. Responses to red, blue and white lights were recorded and relative pupil sizes subsequently determined based on video recordings of each test. Mean testing time was 2.3minutes (range 1.8 to 3.1 minutes), excluding time for dark adaptation. Baseline pupil size in dogs with sudden acquired retinal degeneration syndrome was greater than in normal and predisposed eyes. Pupil constriction was reduced in predisposed compared to normal and sudden acquired retinal degeneration syndrome eyes when stimulated with high-intensity blue light. Compared to normal eyes, those with sudden acquired retinal degeneration syndrome had reduced pupil constriction when stimulated with low- and high-intensity red light, low-intensity blue light and white light. Quantitative measures of pupil function were obtained from healthy and diseased eyes without the need for chemical restraint. Further investigations are warranted to validate the technique and evaluate its use in the management of canine glaucoma.

Conversion: Simulated Method of Exchanging Tourniquet Use for Pressure Dressing Use.

Given little data to assess guidelines, we sought a way to exchange one type of intervention, field tourniquet use, for another, use of a pressure dressing. The study purpose was to test performance of controlling simulated bleeding with a stepwise procedure of tourniquet conversion. An experiment was designed to assess 15 tests of a caregiver making tourniquet-dressing conversions. Tests were divided into trials: tourniquet use and its conversion. In laboratory conditions, the tourniquet trial was care under gunfire; then, the conversion trial was emergency healthcare. A HapMed Leg Tourniquet Trainer simulated a limb amputation. An investigator provided healthcare. Mean (± standard deviation [SD]) test time and blood loss were 9 ± 3.6 minutes and 334 ± 353.9mL, respectively. The first test took 17 minutes. By test number, times decreased; the last six took ≤7 minutes. All tourniquet trials controlled bleeding. Mean (±SD) tourniquet pressure and blood loss were 222 ± 18.0mmHg and 146 ± 40.9mL, respectively. Bleeding remained uncontrolled in one conversion. Initial attempts to wrap a dressing were effective in 73% of tries (n = 11 of 15). Four of 15 wrap attempts (27%) were repeated to troubleshoot bleeding recurrence, and the first three tests required a repetition. Mean (±SD) dressing pressures and blood losses were 141 ± 17.6mmHg and 188 ± 327.4mL, respectively. Unsatisfactory conversion trials had a dressing pressure <137mmHg. Dressings and wraps hid the wound to impair assessment of bleeding. In testing a method of converting a limb tourniquet to a pressure dressing, the caregiver performed faster with experience accrual. The tourniquet results were uniformly good, but conversion results were worse and more varied. Simulating conversion was disappointing on a manikin and indicated that its redesign might be needed to suit this method. The procedural method constituted a start for further development.

A group sequential test for ABR detection

Objective: To detect the auditory brainstem response (ABR) automatically using an innovative sequentially applied Hotelling’s T 2 test, with the overall goal of optimising test time whilst controlling the false-positive rate (FPR).Design: The stage-wise critical decision boundaries for accepting or rejecting the null hypothesis were found using a new approach called the Convolutional Group Sequential Test (CGST). Specificity, sensitivity, and test time were evaluated using simulations and subject recorded data.Study sample: Data consists of click-evoked ABR threshold series from 12 normal hearing adults, and recordings of EEG background activity from 17 normal hearing adults.Results: Reductions in mean test time of up to 40–45% were observed for the sequential test, relative to a conventional “single shot” test where the statistical test is applied to the data just once. To obtain these results, it will occasionally be necessary to run the test to a higher number of stimuli, i.e. the maximum test time needs to be increased.Conclusions: The CGST can be used to control the specificity of a sequentially applied ABR detection method. Doing so can reduce test time, relative to the “single shot” test, when considered across a cohort of test subjects.

The American College of Surgeons Entering Resident Readiness Assessment Program: Development and National Pilot Testing Results.

To assess the readiness of entering residents for clinical responsibilities, the American College of Surgeons (ACS) Division of Education developed the "Entering Resident Readiness Assessment" (ACS-ERRA) Program. ACS-ERRA is an online formative assessment that uses a key features approach to measure clinical decision-making skills and focuses on cases encountered at the beginning of residency. Results can be used to develop learning plans to address areas that may need reinforcement. A national panel of 16 content experts, 3 medical educators, and a psychometrician developed 98 short, key features cases. Each case required medical knowledge to be applied appropriately at challenging decision points during case management. Four pilot testing studies were conducted sequentially to gather validity evidence. Residents from programs across the United States participated in the studies (n = 58, 20, 87, 154, respectively). Results from the pilot studies enabled improvements after each pilot test. For the psychometric pilot (final pilot test), 2 parallel test forms of the ACS-ERRA were administered, each containing 40 cases, resulting in overall mean testing time of 2 hours 2 minutes (SD = 43 min). The mean test score was 61% (SD = 9%) and the G-coefficient reliability was 0.90. Results can be used to identify strengths and weaknesses in residents' decision-making skills and yield valuable information to create individualized learning plans. The data can also support efforts directed at the transition into residency training and inform discussions about levels of supervision. In addition, surgery program directors can use the aggregate test results to make curricular changes.

Skills development of mechanized softwood sawtimber cut-to-length harvester operators on the Highveld of South Africa

ABSTRACTIn this study, a South African pine sawtimber mechanized cut-to-length harvesting operation was analyzed with the objective of modelling productivity development learning curves of beginner operators in both clear-felling and thinning operations. Trainee operators were selected based on the results of an assortment of psychometric testing and subsequently completed several harvester simulator tests. On average, a trainee operator would start at aperformance level (PL) of 60% lower than the population’s mean test time per test (PTmean) and end with a PL of 22% higher than the PTmean. Furthermore, when the PL of an average simulator trainee was measured over a period of 8.4 days or 25.2 tests (three tests per day), the operator’s PL was improved by 200%. Following sufficient simulator training, the operators began working in-field. Thinning operators worked with an average tree size of 0.18 m3, starting at a productivity of 13.71 m3 per productive machine hour excluding delays longer than 5 minutes (PMH5) at month one and increasing their productivity to 38.96 m3·PMH5−1 by the end of month 12. Clear-felling operators worked with an average tree size of 0.54 m3, starting at a productivity of 27.56 m3·PMH5−1 at month one and increased their productivity to 43.75 m3·PMH5−1 by the end of month 12. Ultimately, the thinning and clear-fell operators reached the end of their learning curves after an average of 9 and 6.5 months, and increased their productivity performances by 218% and 104%, respectively. These findings suggest acceptable learning periods and performance increases for beginner harvester operators.