Potential Outliers Research Articles

Obesity and risk for respiratory diseases: a Mendelian randomization study.

No existing comprehensive Mendelian randomization studies have focused on how obesity affects respiratory diseases. BMI and waist circumference, mainly from the UK Biobank, and 35 respiratory diseases from the FinnGen Biobank were subjected to Mendelian randomization analyses. In this study, the inverse variance weighting method was used as the predominant analysis method and was complemented by MR-Egger and weighted median methods. Horizontal pleiotropy and potential outliers were detected by employing the MR-PRESSO method. This study indicated that obesity rises the possibility of acute upper respiratory infections (BMI: OR=1.131, p<0.0001; WC: OR=1.097, p=0.00406), acute sinusitis (BMI: OR=1.161, p=0.000262; WC: OR=1.209, p=0.000263), acute pharyngitis (WC: OR=1.238, p=0.0258), acute laryngitis and tracheitis (BMI: OR=1.202, p=0.0288; WC: OR=1.381, p=0.00192), all influenza (BMI: OR=1.243, p=0.000235; WC: OR=1.206, p=0.0119), viral pneumonia (WC: OR=1.446, p=0.000870), all pneumoniae (BMI: OR=1.174, p <0.0001; WC:OR=1.272, p <0.0001), bacterial pneumoniae (BMI: OR=1.183, p=0.000290; WC: OR=1.274, p<0.0001), acute bronchitis (BMI: OR=1.252, p<0.0001; WC: OR=1.237, p=0.000268), acute unspecified lower respiratory infection (BMI: OR=1.303, p=0.000403), chronic tonsils and adenoids diseases (BMI: OR=1.236, p <0.0001; WC: OR=1.178, p=0.000157), chronic laryngotracheitis and laryngitis (WC: OR=1.300, p=0.00785), COPD (BMI: OR=1.429, p <0.0001; WC: OR=1.591, p <0.0001), asthma (BMI: OR=1.358, p<0.0001; WC: OR=1.515, p <0.0001), necrotic and suppurative conditions of lower respiratory tract (WC: OR=1.405, p=0.0427), pleural effusion (BMI: OR=1.277, p=0.00225; WC: OR=1.561, p<0.0001), pleural plaque (BMI: OR=1.245, p=0.0312), other diseases of the respiratory system (BMI:OR=1.448, p <0.0001; WC: OR=1.590, p <0.0001), and non-small cell lung cancer (BMI: OR=1.262, p=0.00576; WC: OR=1.398, p=0.00181). This study also indicated that obesity decreases the possibility of bronchiectasis (BMI: OR=0.705; p=0.00200). This study revealed that obesity increases the risk of the majority of respiratory diseases (including 20 of all 35 respiratory diseases) and that obesity decreases the risk of bronchiectasis.

Diagnostics for partially linear measurement error models

Partially linear models are useful tools to analyze data from economic, genetic, and other fields. Similar to other data analyses, the identification of influential observations that may be potential outliers is an important step beyond estimation in such models. The objective of this article is to develop some diagnostic measures for identifying influential observations in partially linear models when some of the covariates are measured with errors. Deletion measures are developed based on case deletion, mean shift outlier models, and the corrected likelihood of Nakamura (1990). The performance of the methods is illustrated by an artificial example and a real example.

A revised concept for deriving reference values for internal exposures to chemical substances and its application to population-representative biomonitoring data in German children and adolescents 2014–2017 (GerES V)

HBM reference values, in contrast to toxicologically derived values, are statistically derived values that provide information on the exposure of the population. The exceedance frequency (if applicable for individual population groups) is often a first assessment standard for the local exposure situation for municipalities. More than 25 years have passed since the German Human Biomonitoring Commission (HBMC) formulated the first recommendations for the derivation of population-based reference values (HBM reference values, RV95) for substance concentrations based on HBM studies. A fundamental revision is timely, for several reasons. There have been considerable advances in relevant statistical methods, which meant that previously time-consuming and inaccessible procedures and calculations are now widely available. Furthermore, not all steps for the derivation of HBM reference values were clearly elaborated in the first recommendations. With this revision we intended to achieve a rigorous standardization of the entire process of deriving HBM reference values, also to realise a higher degree of transparency.In accordance with established international practice, it is recommended to use the 95th percentile of the reference distribution as the HBM reference value. To this end, the empirical 95th percentile of a suitable sample should be rounded, ensuring that the rounded value is within the two-sided 95% confidence interval of the percentile. All estimates should be based on distribution-free methods, and the confidence interval should be estimated using a bootstrap approach, if possible, according to the BCa (“bias-corrected and accelerated bootstrap”). A minimum sample size of 80 observations is considered necessary. The entire procedure ensures that the derived HBM reference value is robust against at least two extreme values and can also be used for underlying mixed distributions.If it is known in advance that certain subgroups (different age groups, smokers, etc.) show differing internal exposures, it is recommended that group-specific HBM reference values should be derived. Especially when the sample sizes for individual subgroups are too small, individual datasets with potential outliers can be excluded in advance to homogenize the reference value population.In the second part, new HBM reference values based on data of the German Environmental Survey for Children and Adolescents (GerES V, 2014–2017) were derived in accordance with the revised recommendations. The GerES V is the most recent population-representative monitoring of human exposure to pollutants in Germany on children and adolescents aged 3–17 years (N = 2294). RV95 for GerES V are reported for four subgroups (males/females and 3–11/12-17 years) for 108 different substances including phthalates and alternative plasticisers, metals, organochlorine pesticides, polychlorinated biphenyls (PCB), per- and polyfluoroalkyl substances (PFAS), parabens, aprotic solvents, chlorophenols, polycyclic aromatic hydrocarbons (PAH) and UV filter, in total 135 biomarkers. Algorithms implemented in R were used for the statistics and the determination of the HBM reference values. To facilitate a quality control of the study data, the corresponding R source code is given, together with graphical representations of results.The HBM reference values listed in this article replace earlier RV95 values derived by the HBMC for children and adolescents from data of precedent GerES studies (e.g. published in Apel et al., 2017).

Smoothness-Driven Consensus Based on Compact Representation for Robust Feature Matching.

For robust feature matching, a popular and particularly effective method is to recover smooth functions from the data to differentiate the true correspondences (inliers) from false correspondences (outliers). In the existing works, the well-established regularization theory has been extensively studied and exploited to estimate the functions while controlling its complexity to enforce the smoothness constraint, which has shown prominent advantages in this task. However, despite the theoretical optimality properties, the high complexities in both time and space are induced and become the main obstacle of their application. In this article, we propose a novel method for multivariate regression and point matching, which exploits the sparsity structure of smooth functions. Specifically, we use compact Fourier bases for constructing the function, which inherently allows a coarse-to-fine representation. The smoothness constraint can be explicitly imposed by adopting a few low-frequency bases for representation, resulting in reduced computational complexities of the induced multivariate regression algorithm. To cope with potential gross outliers, we formulate the learning problem into a Bayesian framework with latent variables indicating the inliers and outliers and a mixture model accounting for the distribution of data, where a fast expectation-maximization solution can be derived. Extensive experiments are conducted on synthetic data and real-world image matching, and point set registration datasets, which demonstrates the advantages of our method against the current state-of-the-art methods in terms of both scalability and robustness.

Automated walk-over-weigh system to track daily body mass and growth in grazing steers

Body weight is a critical component for monitoring animal body mass, body condition, nutritional status, and health. However, traditional methods of collecting body weight are stressful, costly, and logistically impractical with cattle on extensive landscapes. Remote systems capable of collecting individual animal body weight provide a potential solution, but automatic systems may collect spurious weight measurements. The objectives of this study were to (1) describe the utility of using a remote weight capture system, (2) develop methods for identifying and removing spurious measurements, and (3) cross-validate automatically collected weights with static chute collected weights. Beef steers (n = 10) were tagged with electronic Radio Frequency Identification tags and grazed for 286 days in an improved pasture (±11.54 ha) containing Bermuda (Cynodon dactylon) and Tall Fescue (Festuca arundinacea) and inter-seeded with Annual Ryegrass (Secale cereale). A walk-over-weigh (WOW) system was designed from a platform scale connected to a TruTest® scale head and paired with a computer to save data files. In total, 5,466 raw weights associated with individuals were collected (0 kg < weight < 1,000 kg). We assessed the use of whole herd and individual means ± 1 standard deviation calculated either daily or over the entire trial to identify potential outliers. Additionally, 3 data smoothing methods were tested: a linear quadratic model, cubic splines, and polynomial regression. Each method was evaluated for total weight events recorded, event frequency, measured body mass and average daily gain. Further, accuracy and precision were calculated using cross-validation with paired chute weights (n = 8). Whole herd means ± 1 SD identified the fewest spurious data points and showed a stronger relationship with paired chute weights (R2 = 0.90) whereas individual daily means ± 1 SD identified the most and exhibited a weaker relationship (R2 = 0.68). Repeated quadratic regression models provided the best results, identifying 3,759 clean weight points and providing an R2 = 0.96 between paired chute and WOW measurements. A real-time simulation analysis found that both individual and the herd means ± 1 SD performed similarly with an average of 30% of data classified as spurious and removed from the analysis, resulting in agreement (R2 = 0.93 for both individual and herd means) between chute and WOW weight measurements. These results indicate the utility of using a simple WOW system to collect data for measuring individual animal body mass and associated metrics, which has implications on making management and marketing decisions in near real-time.

Calibration of photoelastic materials with the disc under diametral compression

Abstract The disc under diametral compression is one of the preferred specimens for the calibration of photoelastic materials. Current calibration methods have some limitations from a metrological point of view, because they do not provide measurement uncertainties and they assume that observations are independent and with the same accuracy. In this work, a new calibration method is proposed to avoid the above-mentioned metrological limitations: The generalized least squares by Lagrange multipliers method. This methodology accepts correlated input quantities or with different accuracy through the input covariance matrix, and it provides, through a least-squares adjustment, the estimates of the quantities to be measured and their associated standard uncertainties. The application of the method shows coherent results and it also provides a chi-square value that can be used to test the consistency of the measurement model, and the normalized deviations between the input estimates and their fitted values, which are a tool to identify potential outliers. Results reveal a great influence of the radius of the specimen on the uncertainty of the stress-optic coefficient measurement, which casts serious doubt on previous affirmations about the disc in compression is preferable over other specimens.

Optimal choice of cotton cultivar for rainfed conditions in Sahelo-Sudanian climate with late planting: a case study in Senegal

AbstractLate planting due to erratic onset of the rainy season is becoming more frequent in the Sahelo-Sudanian climate where cotton is grown, causing seed cotton yield (SCY) loss and higher risk of drought at the end of the crop cycle. Therefore, cultivars should be adapted to late (from July 10) planting date (PD) in Senegal. The aim of this study was to analyse the interaction between genotypes and PD on SCY in Senegal under rainfed conditions. Field experiments were conducted in 2018 and 2019 using a split-plot design (two PDs, eight cultivars) at three experimental stations. Robust analysis of SCY was used to moderate the effect of potential outliers. The average SCY was 1404 kg/ha under early planting, and 714 kg/ha under late planting. The best SCY was obtained under early planting conditions, in environments with good rainfall. The loss due to late planting was significantly affected by cultivar choice. None of the cultivars performed best under both early and late PD. Under early PD, cultivar CS 50 gave the best SCY, while under late PD it was cultivar IRMA Q302. The best performing cultivar on average depended on the proportion of early plantings. A model was developed to identify the best overall cultivar based on the expected proportion of early planting, as a decision support tool for the cotton development company, if only one cultivar is released. The benefit of releasing a second cultivar for late-planted fields is considered.

Data quality control in longitudinal epidemiologic studies: conditional studentized residuals from linear mixed effects models for outlier detection in the setting of pediatric chronic kidney disease

PurposeQuality control in longitudinal cohort studies is critical for valid epidemiologic inference. Conditional studentized residuals (CSRs) derived from linear mixed effects models offer efficient individual-specific quality control. We present the utility of CSRs for outlier detection in an applied example using data from the Chronic Kidney Disease in Children cohort. MethodsLongitudinal linear mixed effects models with glomerular filtration rate (GFR) as the outcome were fit for observations prior to kidney replacement therapy, stratified by nonglomerular or glomerular diagnosis, and for a subset after receiving a kidney transplant. For each model, CSRs were calculated and values ≥±5 were considered potential outliers for further investigation. ResultsA total of 1096 participants contributed 6881 annual measures of GFR across the two diagnostic groups and after transplant. In all models, the fixed effects captured progressive GFR decline. CSRs provided measures of individual-level deviations from the modeled trajectories (random + fixed effects) and were easily visualized in longitudinal plots. A total of 38 potential outliers from 32 participants were detected and further investigated for quality control. ConclusionsThis example demonstrated how longitudinal models can provide CSRs to detect individual-specific outliers. CSRs should be considered as part of quality control for longitudinal epidemiologic studies.

Associations between adiposity, diabetes, lifestyle factors and the risk of gliomas.

Despite numerous observational studies linking adiposity, diabetes, and lifestyle factors with gliomas, the causal associations between them remain uncertain. This study aimed to use two-sample Mendelian randomization (MR) analysis to investigate whether these associations are causal. Specifically, independent genetic variants in body mass index (BMI), waist circumference (WC), type 2 diabetes (T2D), smoking, alcohol, and coffee consumption were extracted from the published genome-wide association studies (GWASs) with genome-wide significance. The corresponding summary-level data for gliomas were available from a GWAS of 1,856 cases and 4,955 controls of European descent from the GliomaScan consortium. Additionally, glioma pathogenesis-related protein 1 data were used for validation, and Radial MR analysis was conducted to examine the potential outlier single-nucleotide polymorphisms (SNPs). One standard deviation (SD) increase in BMI had an odds ratio (OR) of 1.392 (95% confidence interval (CI), 0.935-2.071) for gliomas, while one SD increase in WC had an OR of 0.967 (95% CI, 0.547-1.710). For T2D, a one-unit increase in log-transformed OR had an OR of 0.923 (95% CI, 0.754-1.129). The prevalence of smoking initiation had an OR of 1.703 (95% CI, 0.871-3.326) for gliomas, while the prevalence of alcohol intake frequency had an OR of 0.806 (95% CI, 0.361-1.083), and the prevalence of coffee intake had an OR of 0.268 (95% CI, 0.033-2.140) for gliomas. This study provides evidence that adiposity, T2D, smoking, alcohol drinking, and coffee intake do not play causal roles in the development of gliomas. The findings highlight the importance of reconsidering causal relationships in epidemiological research to better understand the risk factors and prevention strategies for gliomas.

Robust estimation for functional quadratic regression models

Functional quadratic regression models postulate a polynomial relationship rather than a linear one between a scalar response and a functional covariate. As in functional linear regression, vertical and especially high–leverage outliers may affect the classical estimators. For that reason, providing reliable estimators in such situations is an important issue. Taking into account that the functional polynomial model is equivalent to a regression model that is a polynomial of the same order in the functional principal component scores of the predictor processes, our proposal combines robust estimators of the principal directions with robust regression estimators based on a bounded loss function and a preliminary residual scale estimator. Fisher–consistency of the proposed method is derived under mild assumptions. Consistency, asymptotic robustness as well as an expression for the influence function of the related functionals are derived when the covariates have a finite–dimensional expansion. The results of a numerical study show the benefits of the robust proposal over the one based on sample principal directions and least squares for the considered contaminating scenarios. The usefulness of the proposed approach is also illustrated through the analysis of a real data set which reveals that when the potential outliers are removed the classical method behaves very similarly to the robust one computed with all the data.

POTENTIAL IMPACT OF THE IMPLEMENTATION OF AN ELECTRONIC HEALTH RECORD ON NATIONAL JOINT REGISTRY OUTLIER ANALYSES

Electronic Health Records (EHRs) have benefits for hospitals and uptake in the UK is increasing. The National Joint Registry (NJR) monitors implant and surgeon performance and relies on accuracy of data. NJR data are used for identification of potential outliers for both mortality and revision; analyses are adjusted for age, sex, and American Society of Anaesthesiologists score (ASA) and cases with some indications are excluded from analyses. In October 2020, the Royal Devon University Hospitals NHS Foundation Trust “went live” on an EHR, almost eradicating paper from the Trust. This included stopping use of paper NJR forms by creating a bespoke electronic template. We sought to identify discrepancies between operation notes and data input to the NJR in variables that may influence potential outlier analyses.Data input to the NJR from 15/10/2020 to 18/10/2022 for hip procedures were provided by NEC Software Solutions. NJR data were compared to those recorded on operation notes.There were 1067 hip procedures recorded in the NJR (946 primary THRs). Of the primary THRs, discrepancies in indication between NJR and operation note were identified in 139 (15%) cases. Common discrepancies included cases being recorded as osteoarthritis where the true indication was acute trauma (n=63), avascular necrosis (n=14), metastatic cancer/malignancy (n=6) and 21 cases with no recorded indication. We identified 88 cases where the ASA recorded in the NJR differed from the anaesthetic chart. Other inaccuracies were identified including 23 cases missing type of procedure (e.g., primary or revision) and one where revision surgery had been recorded as primary.We identified at least 83 cases that should have been excluded from NJR mortality analyses but were not. Given the low incidence of mortality following primary THR, these cases (with increased risk of death) have the potential to incorrectly identify the hospital as a potential outlier. Discrepancies in ASA may also impact on both revision and mortality outlier calculations. We urge caution to hospitals in the implementation of EHRs and advise regular audit of data sent to the NJR.

Carotid pulse wave velocity measured using a fast single‐slice oblique‐sagittal phase‐contrast MRI is associated with cognitive impairment

AbstractBackgroundArterial stiffness is one of the earliest markers of vascular dysfunction1. Elevated arterial stiffness leads to the transmission of excessive pulsations into the downstream microvasculature, which may be linked to cognitive impairment in aged individuals. Pulse wave velocity (PWV) is an established surrogate of arterial stiffness. To date, PWV measurement at carotid arteries or above has been still limited. Previous work has introduced a fast MRI method to measure carotid PWV (cPWV) using single‐slice oblique‐sagittal phase‐contrast MRI (OS PC‐MRI)3. In this study, we evaluated the association between cPWV and cognitive impairment in an aged group.MethodThe MRI experiments were conducted on Siemens Prisma 3T MRI scanner. A 2min TOF MRI was performed to localize carotid arteries including CCA and ICA. The 3D MR angiogram was reformatted to determine an oblique slice to maximally cover both CCA and ICA (Figure 1a). A single‐slice retrospectively gated 2D OS PC‐MRI with a single in‐plane velocity encoding (CCA to ICA) (Venc = 80cm/s, temporal resolution = 14.22ms) was performed to simultaneously acquire blood velocity waveforms at each location along the CCA‐ICA segment. cPWV was calculated as the inverse slope of the line fitted to the transit time versus distance along the vessel (Figure 1b). 15 elderly participants (age: 71.4 ± 8.5 years) were enrolled in this study. Each participant underwent cognitive assessments including CDR and 13 out of 15 were conducted the MoCA.ResultThere was a significant difference (p = 0.0496) in cPWV between the participants with normal cognition (CDR = 0) and cognition impairment (CDR&gt; = 0.5). Figure 3 shows the scatter plot of cPWV vs. MoCA. Overall, cPWV increased with lower MoCA. However, there was no significance (r = ‐0.5294, p = 0.0767) obtained between cPWV and MoCA given a small sample size and the presence of a suspicious data point indicated by a green circle in Figure 3. After excluding this potential outlier, a significant negative correlation between cPWV and MoCA was obtained (r = ‐0.8113, p = 0.0014).ConclusionThe pilot study demonstrates that increased cPWV is associated with cognitive impairment, suggesting cPWV measured using 2D OS PC‐MRI could be an imaging marker for cognitive impairment.

The mortality burden of cachexia in patients with non-small cell lung cancer: A meta-analysis.

e24075 Background: Cachexia and weight loss have been linked to poor outcomes in patients with cancer. This study’s aim was to conduct a meta-analysis to estimate the risk of mortality associated with cachexia in patients with non-small cell lung cancer (NSCLC) in studies identified through a systematic literature review (SLR). Methods: A feasibility assessment was undertaken to determine the suitability of conducting a meta-analysis evaluating the impact of cachexia, defined per International Consensus criteria (ICC) or previous weight loss (WL) ≥5% (without specifying a 6-month duration), on overall survival (OS) in patients with NSCLC. The meta-analysis was conducted using a random-effects model, considering variability across studies, and using the identified studies as base case. The impact of heterogeneity was evaluated through sensitivity and subgroup analyses. Standard measures of statistical heterogeneity were calculated for each meta-analysis. Results: Of the 40 NSCLC publications identified in the SLR, 20 (50%) used the ICC definition or reported WL ≥5%. Sixteen studies (80%, n = 6,225 patients, published in 2016 to 2021) met the criteria for inclusion in the meta-analysis: 11 (69%) studies per ICC and 5 (31%) studies per WL ≥5%. Combined criteria (ICC or WL ≥5%) were associated with 82% higher mortality risk versus no cachexia or WL &lt; 5% (pooled hazard ratio [95% CI]: 1.82 [1.47, 2.25]). A subgroup analysis of studies using the ICC definition (hazard ratio [95% CI]: 2.26 [1.80, 2.83]) or WL ≥5% (hazard ratio [95% CI]: 1.28 [1.12, 1.46]) showed consistent findings. Although high statistical heterogeneity (I-square = 88%) was observed across studies, this variability could be disregarded because effect estimates (hazard ratios) from included studies were directionally aligned with considerable overlap in CIs of individual studies. A meta-influence analysis did not identify any potential outliers. Results of the sensitivity and subgroup analyses were consistent with the base case analysis. Cumulative meta-analyses by publication year or data collection midpoint identified no time-varying effect, indicating results were robust to the differing timeframes of publication or data collection. No substantial publication bias, measured via funnel plot analysis, was identified in the meta-analysis. Conclusions: Based on thorough methodologic, clinical, and statistical heterogeneity assessments, the meta-analysis results are robust. ICC-defined cachexia or WL ≥5% were associated with inferior OS in patients with NSCLC. Identifying cachexia or WL ≥5% has important implications for prognosis and treatment of NSCLC.

An approach to Preliminary Detection and Confirmation of Potential outliers using R: A case of False Suspicion of outliers in data

An approach to Preliminary Detection and Confirmation of Potential outliers using R: A case of False Suspicion of outliers in data

Identifying Multiple Outliers in Linear Functional Relationship Model using a Robust Clustering Method

Outliers are some observation points outside the usual pattern of the other observations. It is essential to detect outliers as anomalous observations can affect the inference made in the analysis. In this study, we propose an efficient clustering procedure to identify multiple outliers in the linear functional relationship model using the single linkage algorithm with the Euclidean distance as the similarity measure. A new robust cut-off point using the median and median absolute deviation for the tree heights to classify the potential outliers are proposed in this study. Experimental results from the simulation study suggest our proposed method is able to identify the presence of multiple outliers with very small probability of swamping and masking. Application in real data also shows that the proposed clustering method for this linear functional relationship model successfully detects the outliers, thus suggesting the method's practicality in real-world problems.

Artificial benchmark for community detection with outliers (ABCD+o)

The Artificial Benchmark for Community Detection graph (ABCD) is a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs with similar properties as the well-known LFR one, and its main parameter xi can be tuned to mimic its counterpart in the LFR model, the mixing parameter mu. In this paper, we extend the ABCD model to include potential outliers. We perform some exploratory experiments on both the new ABCD+o model as well as a real-world network to show that outliers pose some distinguishable properties. This ensures that our new model may serve as a benchmark of outlier detection algorithms.

Inter- and Intra-observer Agreement of Visual Estimation, Goniometric and Radiographic Measurement of Passive Thumb Metacarpophalangeal Joint Hyperextension.

Background: Reliable methods for measuring range of motion is important for hand therapists. Currently, there is no gold standard for the measurement of thumb metacarpophalangeal joint (MCPJ) hyperextension. We hypothesised that visual and goniometric measurements of thumb MCPJ hyperextension vary greater than 10° from radiographic measurements, and between observers. Methods: Twenty-six fresh-frozen hands were measured by a senior orthopaedic resident and fellowship trained hand surgeon. Passive thumb MCPJ hyperextension was measured by visual estimation, goniometry and axis measurement on a lateral thumb radiograph. Raters were blinded to each other's and their own prior measurements. Descriptive statistics were recorded for measurement type and inter-observer agreement using a two-way intra-class correlation coefficient (ICC). Intra-observer agreement was calculated using concordance correlation coefficient (CCC). Bland-Altman plots identified trends, systemic differences or potential outliers. Results: Mean measurements for both raters were similar for visual estimation and radiographic measurements. Mean goniometric measurements were twice as high for Rater B, and closer to radiographic measurements. For both raters, mean radiographic measurements were 10° greater than the other two methods. For inter-rater agreement, measurements were within 10° most frequently with radiographic measurement, then visual estimates, and least by goniometer measurements. Rater B had better agreement comparing visual and goniometric to radiographic measurements. Conclusions: Radiographic measurement has the best inter-observer agreement and precision for evaluating passive thumb MCPJ hyperextension, especially considering adjunct corrective procedures when performing a soft-tissue basal joint arthroplasty. Rater experience improves precision, but there is still poor agreement between visual estimates and goniometer measurements compared to radiographic measurements, as the former two underestimate hyperextension by 10°. Development of a standard method of clinical measurement is needed to improve reliability.

Establishing new cutoffs for Cohen's d: An application using known effect sizes from trials for improving sleep quality on composite mental health.

Cohen's d conventional effect size cutoffs [small (0.2), medium (0.5), and large (0.8)] might not be representative of the reported distribution of effect sizes across the different areas of health. Effect size cutoffs might vary not only depending on the area of research, but also on the type of intervention and population. That is, they are context dependent. Therefore, we present strategies to redefine small, medium, and large effect size based on 25, 50, and 75th percentile, respectively. We illustrate these techniques applying them to 72 effect sizes, derived from 65 randomized controlled trials described in a recent meta-analysis (10.1016/j.smrv.2021.101556) of improving sleep quality on composite mental health. Such percentiles are equally distanced from the average effect size as suggested by Jacob Cohen and checked for potential attenuation effects (via weight selection model) and outliers (via OutRules). new cutoffs for effect size distribution of -0.177, -0.329, and -0.557, for small, medium, and large effect size were found, respectively. applying Cohen's effect size thresholds (0.2, 0.5, and 0.8) for trials of improving sleep quality on composite mental health might over-estimate effect sizes compared to the real-world context, especially around medium and large effect sizes.

A randomized, controlled trial of low-fat diet for fatigue in multiple sclerosis (P6-3.008)

<h3>Objective:</h3> Determine if a low-fat diet is effective at ameliorating fatigue in people with multiple sclerosis (PwMS) through a randomized controlled trial (RCT). Primary outcome is reduction in fatigue assessed via Modified Fatigue Impact Scale (MFIS) at 16 weeks. <h3>Background:</h3> Fatigue is a common, disabling symptom of multiple sclerosis (MS). A dietary intervention to improve fatigue is desirable given the minimal risk of adverse events. <h3>Design/Methods:</h3> PwMS were assigned to a low-fat diet (active) or wait-list (control) group. The active group received 4 weeks of data collection/nutrition counseling followed by adherence to the diet for 12 weeks. We measured diet adherence using monthly Food Frequency Questionnaire and 24-hr food recall. The principal statistical analysis used linear mixed models, with a random effect for subject to account for the within-subject correlation, in an intent-to-treat (ITT) framework to determine the effect of diet on the outcomes of MFIS and Fatigue Severity Scale (FSS). Sensitivity analysis was conducted by excluding potential outliers in both groups. <h3>Results:</h3> 39 participants were recruited with 20 in the active group and 19 in the control group. At baseline, participants’ mean age was 50 years (±12 years), mean BMI was 31 kg/m² (±7 kg/m²), and mean EDSS was 3.8 (±1.4). Mean MFIS decreased by −4.00 (95% CI: −12.04, 4.04) and mean FSS decreased by −0.41 (95% CI: −1.18, 0.36) from baseline to the end of the RCT in active group compared to control. Sensitivity analysis strengthened the magnitude of association with a mean MFIS decrease of −13.93 (95% CI: −20.65, −7.20) and mean FSS decrease of −1.22 (95% CI: −1.94, −0.50) in active group compared to control. Percent calories from fat decreased by 10.56% (95% CI: −18.50%, −2.97%) in active group compared to control. <h3>Conclusions:</h3> This 16-week long low-fat dietary intervention reduced the fatigue score significantly in the active group compared to controls. <b>Disclosure:</b> Dr. Chen has received research support from OHSU Foundation. Mrs. Chase has nothing to disclose. Dr. Lane has nothing to disclose. The institution of Dr. Wooliscroft has received research support from National Institutes of Health. Claire Adams has nothing to disclose. Mrs. Srikanth has nothing to disclose. Dr. Silbermann has received personal compensation in the range of $10,000-$49,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Neurology. Dr. Silbermann has received research support from VA. Dr. Rice has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Greenwich Pharmaceuticals. Dr. Hollen has nothing to disclose. Allison Fryman has nothing to disclose. Dr. Martin has received research support from Tartar Trust. The institution of Dr. Martin has received research support from OHSU Foundation. Ms. Vong has nothing to disclose. Anna Orban has nothing to disclose. Dr. Khan has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Dompe Pharmaceuticals. The institution of Dr. Khan has received research support from United Therapeutics. The institution of Dr. Khan has received research support from Janssen pharmaceuticals. The institution of Dr. Khan has received research support from Eli Lilly. The institution of Dr. Khan has received research support from 4D Medical. The institution of Dr. Khan has received research support from Regeneron Pharmaceuticals. The institution of Dr. Khan has received research support from BOA Medical. Dr. Horgan has nothing to disclose. Dr. Yadav has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Janssen Pharmaceuticals. Dr. Yadav has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Bristol Myers Squibb Foundation. Dr. Yadav has received personal compensation in the range of $500-$4,999 for serving as a Consultant for EMD-Serono . The institution of Dr. Yadav has received research support from Department of Veterans Affairs. Dr. Yadav has received research support from NIH. Dr. Yadav has received research support from PCORI. Dr. Yadav has received research support from NMSS. The institution of Dr. Yadav has received research support from Department of Veterans Affairs. The institution of Dr. Yadav has received research support from Tykeson Family Foundation Endowed Professorship.

Graph neural networks and implicit neural representation for near-optimal topology prediction over irregular design domains

This paper proposes a deep neural network-based topology optimization acceleration method for irregular design domains that predicts (near-)optimal topologies. A topology optimization problem is a complex non-Euclidean data, which can be embedded in a graph form, and a graph neural network encodes it to Euclidean data such as vectors and matrices. The encoded information is applied to a multi-layer perceptron-based implicit neural representation model, and the multi-layer perceptron approximator predicts the compliance optimal material distribution. The prediction performance of the proposed encoder-approximator architecture is evaluated for several topology optimization problems. The trained network provides 96.6% compliance accuracy, except for 8.0% of the outliers. The two criteria have been investigated to estimate potential outliers, and post-optimization can resolve the outlier within fewer iterations than the original optimization.