Establishing Prediction Intervals for the SpeedWheel Acuity Test in Adults and Children

2021 ◽  
Author(s):  
Magdalena Laura Luethy ◽  
Andreas Schötzau ◽  
Anja Palmowski-Wolfe
Keyword(s):  
Prediction Interval ◽  
Visual Field Loss ◽  
Retinal Disease ◽  
Prediction Intervals ◽  
Linear Effect ◽  
Snellen Acuity ◽  
Single Center Study ◽  
Adults And Children ◽  
Similar Range ◽  
Acuity Test

Abstract Background The SpeedWheel (SW) test is an objective test of visual acuity (VA) using suppression of the optokinetic nystagmus (OKN). Here, we established prediction intervals of the SW measures compared to Snellen acuity in adults and children. Subjects and Methods In this prospective, single center study, subjects aged at least 4 years underwent testing of VA with SW, Landolt-C, and Tumbling-E symbols (Freiburg acuity test: FrACT-C, FrACT-E). Prediction intervals were established for SW compared to FrACT-C or -E and for FrACT-E compared to FrACT-C. Mixed linear effect models were applied for statistical analysis. Results From 241 subjects, 471 eyes were included: median age 36 years, range 4 – 88 years, 43.6% male, 56.4% female. Eyes included were either healthy or had various underlying ophthalmic conditions. Prediction intervals for SW to estimate FrACT-C or -E acuity showed a similar range compared to the prediction interval of FrACT-C for the estimation of FrACT-E acuity. For each acuity step, there was no influence of age. Up to an SW acuity of 0.7 logMAR, 80% of the subjects had a FrACT-C acuity that was at most 1.6 logMAR lines below, and for an SW acuity of 1.0 logMAR, FrACT-C acuity was not worse than 4 logMAR lines. Prediction intervals for eyes with refractive error, cataract, visual field loss and retinal disease did not differ significantly from healthy eyes in contrast to eyes with amblyopia or multiple ophthalmic disorders. SW correlated well to FrACT tests and results of a previous study fell within our prediction estimates. Conclusion Our prediction intervals for SW acuity may be used to estimate Snellen acuity (FrACT-C and -E) in the clinic in adults and children unable to cooperate in other acuity testing.

scholarly journals Control and coordination of fixational eye movements in the Snellen acuity test

2019 ◽  
Vol 19 (10) ◽  
pp. 145a
Author(s):  
Janis Intoy ◽  
Michele A Cox ◽  
Michele Rucci
Keyword(s):  
Eye Movements ◽  
Fixational Eye Movements ◽  
Snellen Acuity ◽  
Acuity Test

Establishing an Objective Measurement of Visual Acuity with a Computerised Optokinetic Nystagmus Suppression Test

2020 ◽  
Vol 237 (04) ◽  
pp. 502-505
Author(s):  
Noemie Schwob ◽  
Anja Palmowski-Wolfe
Keyword(s):  
Visual Acuity ◽  
Eye Movements ◽  
Optokinetic Nystagmus ◽  
Objective Measurement ◽  
Mean Difference ◽  
Alternative Option ◽  
Snellen Acuity ◽  
Suppression Test ◽  
Infrared Cameras ◽  
Acuity Test

Abstract Objective Investigating the correlation between subjective and objective VA (visual acuity) elicited with a newly developed computerised optokinetic nystagmus (OKN) suppression test (“SpeedWheel”) in adults. Methods SpeedWheel presented alternating black/white stripes moving horizontally across a LED screen. Seven VA steps were induced with Bangerter filters placed onto spectacle frames. Magnified eye movements were projected from infrared cameras inside the frames and displayed onto a smartphone. Dots whose size increased in logarithmic steps were superimposed to suppress OKN. Suppression of OKN resulted in the SpeedWheel acuity which was then correlated to Snellen acuity as measured with the Freiburg Acuity test. Results 28 eyes from 14 individuals were tested. FrACT-E correlated well to SpeedWheel (r: 0.89; p < 0.001). Snellen acuity was never lower than 0.14 LogMAR of SpeedWheel values. Bangerter filters showed greater mean difference to both methods indicating that this filter is not as predictable as suggested by the filter value. Conclusion SpeedWheel offers a fast (< 80 sec) and reliable alternative option to measure objective VA.

scholarly journals Prediction Interval Adjustment for Load-Forecasting using Machine Learning

2019 ◽  
Vol 9 (24) ◽  
pp. 5269
Author(s):  
Miguel A. Zuniga-Garcia ◽  
G. Santamaría-Bonfil ◽  
G. Arroyo-Figueroa ◽  
Rafael Batres
Keyword(s):  
Neural Networks ◽  
Association Rules ◽  
Mean Squared Error ◽  
Prediction Interval ◽  
Power Grid ◽  
Load Forecasting ◽  
Main Idea ◽  
Point Load ◽  
Prediction Intervals ◽  
Probabilistic Load

Electricity load-forecasting is an essential tool for effective power grid operation and energy markets. However, the lack of accuracy on the estimation of the electricity demand may cause an excessive or insufficient supply which can produce instabilities in the power grid or cause load cuts. Hence, probabilistic load-forecasting methods have become more relevant since these allow an understanding of not only load-point forecasts but also the uncertainty associated with it. In this paper, we develop a probabilistic load-forecasting method based on Association Rules and Artificial Neural Networks for Short-Term Load Forecasting (2 h ahead). First, neural networks are used to estimate point-load forecasts and the variance between these and observations. Then, using the latter, a simple prediction interval is calculated. Next, association rules are employed to adjust the prediction intervals by exploiting the confidence and support of the association rules. The main idea is to increase certainty regarding predictions, thus reducing prediction interval width in accordance to the rules found. Results show that the presented methodology provides a closer prediction interval without sacrificing accuracy. Prediction interval quality and effectiveness is measured using Prediction Interval Coverage Probability (PICP) and the Dawid–Sebastiani Score (DSS). PICP and DSS per horizon shows that the Adjusted and Normal prediction intervals are similar. Also, probabilistic and point-forecast Means Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics are used. Probabilistic MAE indicates that Adjusted prediction intervals fail by less than 2.5 MW along the horizons, which is not significant if we compare it to the 1.3 MW of the Normal prediction interval failure. Also, probabilistic RMSE shows that the probabilistic error tends to be larger than MAE along the horizons, but the maximum difference between Adjusted and Normal probabilistic RMSE is less than 6 MW, which is also not significant.

Predicting fish abundance using single-pass removal sampling

2000 ◽  
Vol 57 (5) ◽  
pp. 951-961 ◽  
Author(s):  
Matthew G Mitro ◽  
Alexander V Zale
Keyword(s):  
Rainbow Trout ◽  
Prediction Interval ◽  
Prediction Intervals ◽  
Total Abundance ◽  
Simulated Population ◽  
River Section ◽  
Single Pass ◽  
Rainbow Trout Oncorhynchus Mykiss ◽  
Abundance Estimates ◽  
Removal Sampling

Three-pass removal data for juvenile rainbow trout (Oncorhynchus mykiss) along bank areas of the Henrys Fork of the Snake River, Idaho, were used to construct a mean capture probability (MCP) model to predict abundance from single-pass catch data. We evaluated the MCP model by simulation. The precision of the MCP model was poor when predicting abundance within a specific bank unit. MCP model prediction intervals were about 7.5 times greater than three-pass removal intervals. However, the MCP model performed about the same as three-pass removal for predicting total abundance in a river section from multiple bank samples. We evaluated how the MCP model can be used to improve precision of total abundance estimates. Reallocating effort to sample 150 bank units by single-pass removal rather than 50 bank units by three-pass removal resulted in a 48% increase in prediction interval precision for a simulated population of 10 000 fish. Precision also increased when allocating effort to sampling more bank units of smaller length versus fewer bank units of longer length. Sampling 1500 m of bank as one hundred 15-m bank units increased precision by about 28% versus sampling fifty 30-m bank units and by about 50% versus sampling twenty-five 60-m bank units.

scholarly journals ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 701
Author(s):  
Honghai Niu ◽  
Yu Yang ◽  
Lingchao Zeng ◽  
Yiguo Li
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Prediction Interval ◽  
High Reliability ◽  
Prediction Method ◽  
Prediction Intervals ◽  
Probabilistic Prediction ◽  
Data Simulation ◽  
Weighting Coefficients

Wind power has significant randomness. Probabilistic prediction of wind power is necessary to solve the problem of safe and stable power grid dispatching with the integration of large-scale wind power. Therefore, this paper proposes a novel nonparametric probabilistic prediction model for wind power based on extreme learning machine-quantile regression (ELM-QR). Firstly, the ELM-QR models of multiple quantiles are established, and then the new comprehensive index (NCI) is optimized by particle swarm optimization (PSO) to obtain the weighting coefficients corresponding to the lower and upper bounds of the prediction intervals. The final prediction interval is obtained by integrating the outputs of ELM-QR models and the weighting coefficients. Finally, case studies are carried out with the real wind farm operation data, simulation results show that the proposed algorithm can obtain narrower prediction intervals while ensuring high reliability. Through sensitivity analysis and comparison with other algorithms, the effectiveness of the proposed algorithm is further verified.

Efficacy of dural venous sinus stenting in treating idiopathic intracranial hypertension with acute vision loss

2021 ◽  
pp. 197140092110269
Author(s):  
Aqib H Zehri ◽  
Katriel E Lee ◽  
Jeff Kartchner ◽  
Madison Arnel ◽  
Timothy Martin ◽  
...  
Keyword(s):  
Visual Acuity ◽  
Intracranial Hypertension ◽  
Idiopathic Intracranial Hypertension ◽  
Vision Loss ◽  
Visual Field Loss ◽  
Venous Sinus ◽  
Snellen Acuity ◽  
Average Patient ◽  
Sinus Stenosis ◽  
Dural Venous Sinus

Introduction Dural venous sinus stenting (VSS) is an effective, durable treatment for patients with idiopathic intracranial hypertension (IIH) due to underlying venous sinus stenosis. However, the use of venous sinus stenting to treat IIH with acute vision loss has rarely been described. Methods A retrospective chart analysis identified patients who received VSS for fulminant IIH, defined as acute (< 8 weeks) visual field loss to within the central 5° and/or a decrease in visual acuity to less than or equal to 20/50 in either eye in the presence of papilledema. Results Ten patients were identified with average patient age of 31.0 years, and all except one were female. Mean body mass index was 41.2 kg/m2. All patients presented with vision loss and some with headache and tinnitus. The average trans-stenotic gradient pre-stenting was 28.7 mmHg (range 9–62 mmHg). All patients had diminished or resolved venous gradients immediately following the procedure. At mean follow-up of 60.5 weeks, 100% had improvements in papilledema, 80.0% had subjective vision improvement, 55.6% had headache improvement and 87.5% had tinnitus improvement. 90.0% had stable or improved visual acuity in both eyes with a mean post-stenting Snellen acuity of 20/30 and an average gain of 3 lines Snellen acuity post-stenting (95% confidence intervals 0.1185–0.4286, p = 0.0018). Two patients (20.0%) required further surgical treatment (cerebrospinal shunting and/or stenting) after their first stenting procedure. Conclusions This series suggests that VSS is feasible in patients presenting with IIH and acute vision loss with a fairly low complication rate and satisfactory clinical outcomes.

scholarly journals The Role of Microsaccades in the Snellen Acuity Test

2017 ◽  
Vol 17 (10) ◽  
pp. 920
Author(s):  
Janis Intoy ◽  
Michele Rucci
Keyword(s):  
Snellen Acuity ◽  
Acuity Test

scholarly journals Evaluating agreement between bodies of evidence from randomised controlled trials and cohort studies in nutrition research: meta-epidemiological study

BMJ ◽  
2021 ◽  
pp. n1864
Author(s):  
Lukas Schwingshackl ◽  
Sara Balduzzi ◽  
Jessica Beyerbach ◽  
Nils Bröckelmann ◽  
Sarah S Werner ◽  
...  
Keyword(s):  
Cohort Studies ◽  
Randomised Controlled Trials ◽  
Prediction Interval ◽  
Prediction Intervals ◽  
Controlled Trials ◽  
Nutrition Research ◽  
The Difference ◽  
Bodies Of Evidence ◽  
Randomised Controlled ◽  
Risk Ratios

Abstract Objective To evaluate the agreement between diet-disease effect estimates of bodies of evidence from randomised controlled trials and those from cohort studies in nutrition research, and to investigate potential factors for disagreement. Design Meta-epidemiological study. Data sources Cochrane Database of Systematic Reviews, and Medline. Review methods Population, intervention or exposure, comparator, outcome (PI/ECO) elements from a body of evidence from cohort studies (BoE(CS)) were matched with corresponding elements of a body of evidence from randomised controlled trials (BoE(RCT)). Pooled ratio of risk ratios or difference of mean differences across all diet-disease outcome pairs were calculated. Subgroup analyses were conducted to explore factors for disagreement. Heterogeneity was assessed through I 2 and τ 2 . Prediction intervals were calculated to assess the range of possible values for the difference in the results between evidence from randomised controlled trials and evidence from cohort studies in future comparisons. Results 97 diet-disease outcome pairs (that is, matched BoE(RCT) and BoE(CS)) were identified overall. For binary outcomes, the pooled ratio of risk ratios comparing estimates from BoE(RCT) with BoE(CS) was 1.09 (95% confidence interval 1.04 to 1.14; I 2 =68%; τ 2 =0.021; 95% prediction interval 0.81 to 1.46). The prediction interval indicated that the difference could be much more substantial, in either direction. We further explored heterogeneity and found that PI/ECO dissimilarities, especially for the comparisons of dietary supplements in randomised controlled trials and nutrient status in cohort studies, explained most of the differences. When the type of intake or exposure between both types of evidence was identical, the estimates were similar. For continuous outcomes, small differences were observed between randomised controlled trials and cohort studies. Conclusion On average, the difference in pooled results between estimates from BoE(RCT) and BoE(CS) was small. But wide prediction intervals and some substantial statistical heterogeneity in cohort studies indicate that important differences or potential bias in individual comparisons or studies cannot be excluded. Observed differences were mainly driven by dissimilarities in population, intervention or exposure, comparator, and outcome. These findings could help researchers further understand the integration of such evidence into prospective nutrition evidence syntheses and improve evidence based dietary guidelines.

scholarly journals Prediction intervals for random-effects meta-analysis: A confidence distribution approach

2018 ◽  
Vol 28 (6) ◽  
pp. 1689-1702 ◽  
Author(s):  
Kengo Nagashima ◽  
Hisashi Noma ◽  
Toshi A Furukawa
Keyword(s):  
Random Effects ◽  
Prediction Interval ◽  
Meta Analysis ◽  
Prediction Intervals ◽  
Point Estimate ◽  
Random Effects Models ◽  
Confidence Distribution ◽  
Large Sample Approximation ◽  
Sample Approximation ◽  
Meta Analyses

Prediction intervals are commonly used in meta-analysis with random-effects models. One widely used method, the Higgins–Thompson–Spiegelhalter prediction interval, replaces the heterogeneity parameter with its point estimate, but its validity strongly depends on a large sample approximation. This is a weakness in meta-analyses with few studies. We propose an alternative based on bootstrap and show by simulations that its coverage is close to the nominal level, unlike the Higgins–Thompson–Spiegelhalter method and its extensions. The proposed method was applied in three meta-analyses.

scholarly journals Assessing the impact of case-mix heterogeneity in individual participant data meta-analysis: Novel use of I2 statistic and prediction interval

2020 ◽  
Vol 2 (1) ◽  
pp. 12-30
Author(s):  
Tat-Thang Vo ◽  
Raphaël Porcher ◽  
Stijn Vansteelandt
Keyword(s):  
Prediction Interval ◽  
Meta Analysis ◽  
Target Population ◽  
Prediction Intervals ◽  
Case Mix ◽  
Individual Participant Data ◽  
New Approaches ◽  
Statistical Heterogeneity ◽  
Individual Participant ◽  
The Impact

Case mix differences between trials form an important factor that contributes to the statistical heterogeneity observed in a meta-analysis. In this paper, we propose two methods to assess whether important heterogeneity would remain if the different trials in the meta-analysis were conducted in one common population defined by a given case-mix. To achieve this goal, we first standardize results of different trials over the case-mix of a target population. We then quantify the amount of heterogeneity arising from case-mix and beyond case-mix reasons by using corresponding I2 statistics and prediction intervals. These new approaches enable a better understanding of the overall heterogeneity between trial results, and can be used to support standard heterogeneity assessments in individual participant data meta-analysis practice.

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