Suboptimal Fit Research Articles

Validation of the Instructional Materials Motivation Survey among Medicine Resident Physicians

Purpose The Instructional Materials Motivation Survey (IMMS) was developed to measure motivational characteristics of a learning activity, building on Keller's Attention, Relevance, Confidence, Satisfaction (ARCS) motivation model. We aimed to validate IMMS scores using validity evidence of internal structure and relations with other variables. Methods Participants were internal medicine and family medicine residents who completed the IMMS following an online module on outpatient medicine, from 2005 to 2009. We used confirmatory factor analysis (CFA) to examine model fit using half the data (split-sample approach). Finding suboptimal fit, we conducted exploratory factor analysis (EFA) and developed a revised instrument. We evaluated this instrument with CFA using the remaining data. Associations were evaluated between IMMS scores and knowledge and other measures of motivation (Motivated Strategies for Learning Questionnaire, MSLQ). All analyses accounted for repeated measures on subjects. Results There were 242 participants. Although internal consistency reliabilities were good (Cronbach alpha ≥0.70), CFA of the original 36-item, 4-domain instrument revealed poor model fit for data sample 1. EFA found that reverse-scored items clustered strongly together. Further EFA using data sample 1, followed by CFA using data sample 2, found good fit for a 13-item, 4-domain model that omitted reverse-scored items (standardized root mean square residual 0.045, root mean square error of approximation 0.066, comparative fit index 0.96). Linear regression confirmed positive, statistically significant associations for most hypothesized relationships, including IMMS total with knowledge (r=0.19) and MSLQ total (r=0.53; both p<.001). Examination of reverse-scored items suggested participant inattention but not acquiescence. Conclusions IMMS scores show good reliability and relations with other variables. However, the hypothesized and empirical factor structures do not align, and reverse-scored items show particularly poor fit. A 13-item, 4-domain scale omitting reverse-scored items showed good model fit.

Delving into the Complexity of Analogical Reasoning: A Detailed Exploration with the Generalized Multicomponent Latent Trait Model for Diagnosis.

Research on analogical reasoning has facilitated the understanding of response processes such as pattern identification and creative problem solving, emerging as an intelligence predictor. While analogical tests traditionally combine various composition rules for item generation, current statistical models like the Logistic Latent Trait Model (LLTM) and Embretson's Multicomponent Latent Trait Model for Diagnosis (MLTM-D) face limitations in handling the inherent complexity of these processes, resulting in suboptimal model fit and interpretation. The primary aim of this research was to extend Embretson's MLTM-D to encompass complex multidimensional models that allow the estimation of item parameters. Concretely, we developed a three-parameter (3PL) version of the MLTM-D that provides more informative interpretations of participant response processes. We developed the Generalized Multicomponent Latent Trait Model for Diagnosis (GMLTM-D), which is a statistical model that extends Embretson's multicomponent model to explore complex analogical theories. The GMLTM-D was compared with LLTM and MLTM-D using data from a previous study of a figural analogical reasoning test composed of 27 items based on five composition rules: figure rotation, trapezoidal rotation, reflection, segment subtraction, and point movement. Additionally, we provide an R package (GMLTM) for conducting Bayesian estimation of the models mentioned. The GMLTM-D more accurately replicated the observed data compared to the Bayesian versions of LLTM and MLTM-D, demonstrating a better model fit and superior predictive accuracy. Therefore, the GMLTM-D is a reliable model for analyzing analogical reasoning data and calibrating intelligence tests. The GMLTM-D embraces the complexity of real data and enhances the understanding of examinees' response processes.

A - 45 Rasch Analysis of the Brief Resilience Scale in Adolescents with Sports-Related Concussion

Abstract Purpose Accurate measurement of latent abilities is essential for valid inference. The Brief Resilience Scale (BRS) assesses the “ability to bounce back”from stressful situations, and resilience may play a role in concussion recovery. Questions regarding the factor structure of the BRS have been explored in other populations, but evidence of its measurement quality in concussion is lacking. The present study investigated the psychometric properties of the BRS in concussed adolescents within a Rasch framework. Method 927 participants aged 12 to 18 (M = 14.7; 50.3% female) underwent clinical evaluation within 30 days of injury in the ConTex study. The BRS contains 6 items rated on a 5-point Likert scale (Strongly Disagree to Strongly Agree), half of which are reverse scored. The Rasch Rating Scale Model was applied and item fit statistics and dimensionality were examined to assess the degree of fit of the data to the Rasch model. Results Item difficulties ranged −0.82 to 0.77. Item fit was adequate based on mean-square infit and outfit values. The BRS demonstrated sufficient targeting except for participants at the highest and lowest range of latent ability. Unidimensionality testing indicated the presence of a secondary dimension. Conclusions The BRS showed suboptimal fit with the Rasch model based on lack of unidimensionality. However, violations likely reflect item wording effects and not a substantive dimension that merits scale modification. Application of Rasch and other item response theory methodologies can enable development of more psychometrically sound and clinically informative tools for use in the context of concussion outcomes.

Validation of the motivated strategies for learning questionnaire and instructional materials motivation survey

Purpose To validate the Motivated Strategies for Learning Questionnaire (MSLQ), which measures learner motivations; and the Instructional Materials Motivation Survey (IMMS), which measures the motivational properties of educational activities. Methods Participants (333 pharmacists, physicians, and advanced practice providers) completed the MSLQ, IMMS, Congruence-Personalization Questionnaire (CPQ), and a knowledge test immediately following an online learning module (April 2021). We randomly divided data for split-sample analysis using confirmatory factor analysis (CFA), exploratory factor analysis (EFA), and the multitrait-multimethod matrix. Results Cronbach alpha was ≥0.70 for most domains. CFA using sample 1 demonstrated suboptimal fit for both instruments, including 3 negatively-worded IMMS items with particularly low loadings. Revised IMMS (RIMMS) scores (which omit negatively-worded items) demonstrated better fit. Guided by EFA, we identified a novel 3-domain, 11-item ‘MSLQ-Short Form-Revised’ (MSLQ-SFR, with domains: Interest, Self-efficacy, and Attribution) and the 4-domain, 12-item RIMMS as the best models. CFA using sample 2 confirmed good fit. Correlations among MSLQ-SFR, RIMMS, and CPQ scores aligned with predictions; correlations with knowledge scores were small. Conclusions Original MSLQ and IMMS scores show poor model fit, with negatively-worded items notably divergent. Revised, shorter models—the MSLQ-SFR and RIMMS—show satisfactory model fit (internal structure) and relations with other variables.

Assessing age discrimination in workplaces: psychometric exploration of the Workplace Age Discrimination Scale (WADS-G).

In contemporary workplaces characterized by diverse age groups working collaboratively, the assessment of age discrimination as an interpersonal phenomenon has gained heightened significance. This study focuses on adapting and scrutinizing the psychometric properties of the German iteration of the Workplace Age Discrimination Scale (WADS-G). Comprehensive Confirmatory Factor Analysis (CFA) results affirm a robust fit for the unidimensional model. Convergent validity is established through correlations between WADS-G scores and related instruments, while discriminant validity is evidenced by its lack of association with extraversion. Noteworthy findings include a positive correlation with turnover intention and negative correlations with job satisfaction, occupational self-efficacy, and organizational affective commitment. Despite its merits, the predictive efficacy of the WADS-G is notably inferior when juxtaposed with the Workplace Incivility Scale. Its explanatory power for turnover intention is constrained when accounting for variables such as job satisfaction, work environment, neuroticism, and core self-evaluation. Although measurement invariance testing across gender groups reveals scalar to strict measurement invariance, the examination across age groups indicates metric invariance. However, Confirmatory Factor Analyses for the 18-30 and 50+ age groups, central to the research emphasis, reveal suboptimal model fit. These outcomes prompt a nuanced discussion on whether the WADS-G aptly captures age-discriminatory experiences across diverse age and gender cohorts among employees.

A critical note on meal criteria in pigs: Which behaviours do they perform during feeder visit intervals?

The feeding behaviour of pigs can be continuously recorded using sensors, providing promising avenues for automatic monitoring of pig performance and welfare. To utilise this potential, however, the data must be cleaned and aggregated meaningfully. A common aggregation is from visits into meals, in which visits separated by intervals shorter than a meal criterion (s or min) are merged. Methods to determine the criterion and the criteria themselves vary widely between studies, and have been applied indiscriminately or only when no pen mates visited the feeder during the interval. Aggregation choices should be biologically relevant, but there is no empirical knowledge on how pigs behave during these intervals or how the intervals are influenced by feeder competition. This study had three aims: 1) test the method that classifies intervals using a three-part probability density function (short, intermediate and long intervals); 2) determine whether feeder competition differed between interval types and application methods; and 3) describe and compare the behaviours of pigs between intervals. Visit intervals were obtained from 110 barrows in ten pens with one IVOGⓇ electronic feeding station each. A three-part probability density function was fitted to the log-transformed intervals, and its fit was assessed visually. For each pig, a short and an intermediate interval were selected for behavioural observations from camera. We found that pigs had relatively more intermediate intervals (1–28 min) than cows and that the fit of the three-part function was suboptimal. Nevertheless, identified meal criteria were in similar ranges as for other species. Intermediate intervals were more often initiated by displacements than short intervals (≤1 min), and there was more aggression and less pen exploration if pen mates visited the feeder during these intervals. Short intervals reflected interruptions in feeding behaviour, shown by standing (e.g. vigilance/chewing outside the feeder) and pen exploration (e.g. rooting, searching for pellets), while intermediate intervals contained non-feeding behaviours, such as social nosing, drinking and, predominantly, lying inactive. We conclude that intermediate intervals indicated completed feeding bouts, while short intervals reflected continued feeding-focused behaviours. Therefore, only visits separated by short intervals should be merged into meals. The exact criterion depends on the dataset but may, considering the suboptimal fit of the function, be more precisely determined using other methods. Whether visits should be merged indiscriminately or only when no pen mates entered the EFS during the interval depends on whether competition effects are of interest in the study.

Monitoring the recovery-stress states of athletes: Psychometric properties of the acute recovery and stress scale and short recovery stress scale among Dutch and Flemish athletes

ABSTRACT The Acute Recovery and Stress Scale (ARSS) and the Short Recovery and Stress Scale (SRSS) are recently-introduced instruments to monitor recovery and stress processes in athletes. In this study, our aims were to replicate and extend previous psychometric assessments of the instruments, by incorporating recovery and stress dimensions into one model. Therefore, we conducted five confirmatory factor analyses (CFA) and determined structural validity, internal consistency, and construct validity. Dutch and Flemish athletes (N = 385, 213 females, 170 males, 2 others, 21.03 ± 5.44 years) completed the translated ARSS and SRSS, the Recovery Stress Questionnaire for Athletes (RESTQ-Sport-76), the Rating of Perceived Exertion (RPE) and the Total Quality of Recovery (TQR). There was a good model fit for the replicated CFA, sub-optimal model fit for the models that incorporated recovery and stress into one model, and satisfactory internal consistency (α=.75 – .87). The correlations within and between the ARSS and SRSS, as well as between the ARSS/SRSS and the RESTQ-Sport-76 (r = .31 – -.77 for the ARSS, r = .28 – -.63 for the SRSS), the RPE (r = .19 – -.23), and the TQR (r = .63 – -.63) also supported construct validity. The combined findings support the use of the ARSS and SRSS to assess stress and recovery in sports-related research and practice.

A novel model-based equation for size dependent mean recovery coefficients for spheres and other shapes

BackgroundIn NM-imaging, theoretical curves for the recovery coefficient (RC) of the signal maximum and mean are known for spheres and cubes, if a 3D Gaussian PSF is assumed. The RC of the maximum is also known for cylinders. For these and other shapes empirical equations with one or two fit-parameters have been utilized. MethodsAn equation for the RC for large objects of arbitrary shape is derived and generalized into an empirical equation for smaller objects, which is verified by numerical simulations. The proposed equation is compared to published results on SPECT kidney phantom measurements and to PET measurements on the NEMA IEC PET body phantom with six spheres. ResultsThe signal loss (1-RC) for large spheres is inversely proportional to the radius, where the slope is proportional to the FWHM of the spatial resolution. For non-spherical shapes the generalized instead of the volume equivalent radius should be utilized. For smaller objects, an equation with one added empirical fit-parameter is presented. It is demonstrated that the EANM-guidelines’ two-parameter logistic function results in a poor fit if the theoretical slope and inverse proportionality are forced and it gives a suboptimal fit when both parameters are fitted. ConclusionsA novel model-based equation for the mean RC-curve is derived. It can be used for arbitrary shapes as long as the sphericity is taken into account and it is accurate down to RC = 10 %. One parameter is directly related to the spatial resolution, while the other is a shape depending fit-parameter.

Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm

Traditional linear regression and neural network models demonstrate suboptimal fit and lower predictive accuracy while the quality of electrolytic copper is estimated. A more dependable and accurate model is essential for these challenges. Notably, the maximum information coefficient was employed initially to discern the non-linear correlation between the nineteen factors influencing electrolytic copper quality and the five quality control indicators. Additionally, the random forest algorithm elucidated the primary factors governing electrolytic copper quality. A hybrid model, integrating particle swarm optimization with least square support vector machine, was devised to predict electrolytic copper quality based on the nineteen factors. Concurrently, a hybrid model combining random forest and relevance vector machine was developed, focusing on primary control factors. The outcomes indicate that the random forest algorithm identified five principal factors governing electrolytic copper quality, corroborated by the non-linear correlation analysis via the maximum information coefficient. The predictive accuracy of the relevance vector machine model, when accounting for all nineteen factors, was comparable to the particle swarm optimization—least square support vector machine model, and surpassed both the conventional linear regression and neural network models. The predictive error for the random forest-relevance vector machine hybrid model was notably less than the sole relevance vector machine model, with the error index being under 5%. The intricate non-linear variation pattern of electrolytic copper quality, influenced by numerous factors, was unveiled. The advanced random forest-relevance vector machine hybrid model circumvents the deficiencies seen in conventional models. The findings furnish valuable insights for electrolytic copper quality management.

Likelihood Maximization of Lifetime Distributions With Bathtub-Shaped Failure Rate

Equipment lifetime distributions with bathtub-shaped failure rate can be fitted to data by the maximum likelihood criterion. In the literature, a commonly used method is to find a point in the parameter space where the partial derivatives of the log-likelihood function are zero. As the log-likelihood function is typically nonconvex, this approach may yield a suboptimal fit. In this work, we maximize the log-likelihood function, using a multistart of 100 optimization procedures, by three nonlinear optimization algorithms: 1) Nelder–Mead with adaptive parameters; 2) sequential least squares quadratic programming (SLSQP); 3) limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm with box constraints (L-BFGS-B). We perform a systematic study of refitting ten key lifetime distributions with bathtub-shaped failure rate from the literature to two widely studied datasets. The multistart nonlinear optimization yields better fits than those reported in the literature in 14 out of 19 distribution-dataset pairs, for which reference parameters are available. Based on the results, if gradient information of the log-likelihood function is available, our recommended optimization algorithm for the purpose is SLSQP.

The Danish Version of the 20-Item Prosopagnosia Index (PI20): Translation, Validation and a Link to Face Perception

Developmental prosopagnosia (DP) is a neurodevelopmental condition characterized by face recognition problems. Psychometrically sound self-report measures of face recognition problems are important tools in classification of DP. A widely used measure of such problems is the 20-item prosopagnosia index (PI20). Here, we present a Danish translation of the PI20 (PI20DK). We administered the PI20DK alongside three objective measures of face and object processing performance to 119 participants to validate the PI20DK. Further, we assess the underlying factor structure of the PI20DK. Finally, as the first study in the field, we investigate the association between self-reported face recognition ability and face perception performance. The project was preregistered prior to data collection. The results suggest excellent convergent validity, discriminant validity and internal consistency for the PI20DK. A confirmatory factor analysis, however, indicates a suboptimal fit of the PI20DK to a one factor solution. An investigation of the association between the PI20DK and face perception suggests that the poor fit may reflect that the PI20DK measures problems with face recognition in general and not specifically face memory problems.

Sub-optimal femoral fit in total knee arthroplasty, a systematic review of human femoral data vs off-the-shelf contemporary femoral components

PurposeThe purpose of the current study is to investigate the inadequacy of fit between the human distal femur and the knee implants offerings and describe the available strategies to overcome this issue.MethodsA systematic research of the literature was performed to identify studies reporting morphologic measures of the distal femur. Studies were excluded if they included unhealthy knees or the morphological analysis did not report the two key dimensions to identify the patient’s unique anatomy: AP length and mediolateral (ML) width. Clinically relevant component overhang or underhang was considered when the metal-bone mismatch was > 3 mm as described in the literature.ResultsSix studies with anthropometric analysis of 1395 distal femurs met the inclusion criteria. The analysis revealed that by employing the available sizes of four current “state-of-the-art” primary off-the shelf (OTS) femoral implants up to 13–41% would show underhang and 9–27% overhang clinically relevant and the introduction of narrower sizes did not reduce this percentage of underhang but improved the overhang rate of 10–15%.ConclusionsWhenever an ML/AP mismatch in encountered in the operating room, adaptations are needed, and these bring about deleterious biomechanical and clinical complications. Therefore, this study highlights the need for implants design with multiple ML offerings per AP size, since they provide not only more sizes options but more femoral shapes to match the different ML sizes of the distal femur, compared to designs with single ML offerings for a given femoral AP dimension.

P2/N95 filtering facepiece respirators: Results of a large-scale quantitative mask fit testing program in Australian health care workers

In response to the COVID-19 pandemic, 6,287 Australian health care workers (HCWs) were fit tested to N95 filtering facepiece respirators (FFRs). This study determined how readily HCWs were fitted to 8 FFRs and how age and sex influenced testing. HCWs were fit tested following the quantitative OSHA protocol. After bivariate analysis, a logistic regression model assessed the effect of FFR model, HCW age and sex on fit test results. Of 4,198 female and 2,089 male HCWs tested, 93.3% were successfully fitted. Fifty-five percent passed the first FFR, 21% required 2 and 23% required testing on 3 or more models. Males were 15% less likely to pass compared to females (P < .001). Individuals aged 18-29 were significantly more likely to pass compared to colleagues aged 30-59. Cup-style 3M 1860S was the most suitable model (95% CI: 1.94, 2.54) while the duckbill BSN TN01-11 was most likely to fail (95% CI: 0.11, 0.15). Current N95 FFRs exhibit suboptimal fit such that a large proportion (45%) of HCWs require testing on multiple models. Older age and male sex were associated with significantly higher fit failure rates. QNFT programs should consider HCW characteristics like sex, age, racial and facial anthropometric measurements to improve the protection of the health workforce.

3D virtual fit assessment and modeling: liquid cooling and ventilation garment

PurposeThe suboptimal fit of a spacesuit can interfere with a crewmember's performance and is regarded as a potential risk factor for injury. To quantify suit fit, a virtual fit assessment model was previously developed to identify suit-to-body contact and interference using 3D human body scans and suit CAD models. However, ancillary suit components and garments worn inside of the suit have not been incorporated.Design/methodology/approachThis study was conducted to predict a 3D model of the liquid cooling and ventilation garment (LCVG) from an arbitrary person's body scan. A total of 14 subjects were scanned in a scan wear and LCVG condition. A statistical model was generated using principal component analysis and random forest regression technique.FindingsThe model was able to predict the geometry of the LCVG layer at the accuracy of 5.3 cm maximum error and 1.7 cm root mean square error. The errors were more pronounced for the arms and lower torso, while the thighs and upper torso regions, which are critical for suit fit assessments, show more accurate predictions. A case study of suit fit with and without the LCVG model demonstrated that the new model can enhance the scope and accuracy of future spacesuit assessments.Originality/valueThe capabilities resulting from these modeling techniques would greatly expand the assessments of fit of the garment on various anthropometries. The results from this study can significantly improve the design process modeling and initial suit sizing efforts to optimize crew performance during extravehicular activity training and missions.

Assessing College Students’ Social and Emotional Strengths: A Cross-Cultural Comparison from Mexico, United States, and Spain

&lt;p style="text-align:justify"&gt;Endeavors supporting college students’ positive psychosocial development are gaining attention and investment in various countries and social contexts. Higher education experiences provide new academic, social, and vocational advancement opportunities at a critical developmental stage. However, higher education can also cause distress due to the challenges and stressors present during this new stage of increased independence. The Social Emotional Health Survey-Higher Education (SEHS-HE) assesses the core psychosocial strengths of individuals transitioning from secondary schools into institutions of higher education (IHE) to aid campus student support services. The present study sought to extend the SEHS-HE research by examining its application with college student samples from Mexico (n = 4,207), United States (n = 1,638), and Spain (n = 1,734). Confirmatory factor analyses investigated the hypothesized SEHS-HE higher-order factor model. The Mexico sample returned an acceptable model fit, but the USA and Spain samples had a suboptimal fit; hence, we explored alternative models. A two-level structure had full invariance for all three samples. This study extends the current scholarship on the conceptual model and psychometric properties of SEHS-HE. The discussion focuses on implications for future research to enhance SEHS-HE in national and cross-national research and practice.&lt;/p&gt;

Do Career Demands and Career Choices Always Coincide? A Matching Perspective Based on Career Anchors and Job Characteristics

Career choice is an important behavior for people wanting to develop their social life and is a key link to doing so. The matching of career choice with an individual’s real work demands will have a significant impact on the development of individuals, organizations, and society. However, at this stage, there are few studies on this matching situation. From the perspective of the matching of career anchors and job characteristics, this study explored the distribution and different characteristics of employees’ career demands and their career choices through a survey of 407 employees, and further discussed the matching status of these. The results of the study are as follows: (1) Individual career demands (career anchors) presented three attributes: single, multiple, and unclear. Among the single career anchor types, life anchors had the largest proportion, while among multiple career anchor individuals, individuals with both challenge anchors and service anchors accounted for the majority. (2) Individual career demands (career anchors) were significantly different across most demographic variables and organizational/work variables. (3) Deviations between employees’ career demands and their career choices seemed to be a common phenomenon, with the highest degree of fit (62.79%) with the entrepreneurial creativity anchor and the lowest degree of fit (21.28%) with the lifestyle anchor. In addition, in an analysis of three job fit characteristics, the entrepreneurial creativity anchor had a significant preference for managerial characteristics, whereas the challenge anchor had a significant preference for technological characteristics. The degrees of fit of the other anchors were characterized by the frequency of ‘right suboptimal fit’ being larger than that of ‘left suboptimal fit’. In other words, a specific career anchor had a significant preference for job characteristics matched by the right career anchor, with the midpoint of the career anchor octagon model defining the angle of observation. This study provides a reference for human resource management departments and for employees’ recognition and planning of career anchors.

Design and evaluation of 3D printable patient-specific applicators for gynecologic HDR brachytherapy.

The purpose of this study is to improve dose distribution and organ-at-risk sparing during gynecologic HDR brachytherapy with patient-specific applicators. The majority of applicators used today are generic in design and do not allow for dose modulation for patient-specific shaping of dose distributions. Their performance might be adjusted with commercially available wedge shields; however, this provides dose modulation in the orthogonal plane only and does not allow for variation along the length of the applicator. Generic applicators are available only in standard sizes and geometries, and provide suboptimal patient fit with limited dose modulation. In this paper we use Monte Carlo modeling for comprehensive characterization of radiologic properties of various 3D printable biocompatible and sterilizable materials with comparison to water. Based on these results, we choose the optimal set of materials for a patient-specific applicator. We develop a novel method to design the patient-specific applicator without incurring a significant increase in treatment time or changes to clinical workflow. Finally, using an example of two selected vaginal cancers, we compare the performance of patient-specific and water-equivalent applicators in terms of target coverage and rectum sparing. In the energy range from 1MeV to 4MeV, all materials have similar attenuation coefficients. In the range from ~2keV to 1MeV and above 4MeV, tungsten-polylactic acid composite (WPLA) was seen to have the highest attenuation coefficient. The dose distribution of the water-equivalent applicator was found to be symmetric about its central axis. At the same time patient-specific shielded applicators exhibit well-modulated dose distributions. Their isodose lines are seen to spread radially into the patient, while merging close to the applicator surface, where WPLA shielding has been applied. The patient-specific cylinders provide comparable dose to the target, while offering advanced healthy tissue sparing, not achievable with the generic design.

Ultrasound assessment of gastric volumes of thick fluids: Validating a prediction model.

Enteral nutrition is essential in the treatment of critically ill patients. Current methods to monitor enteral nutrition such as aspiration of residual volume may be inaccurate. Gastric ultrasonography estimates total gastric fluid volume using the Perlas model, but this model is validated for clear fluids only, and its accuracy for measuring thick fluids is unknown. The primary aim of this study was to evaluate the Perlas model for gastric volume estimation of enteral nutrition, a thick fluid product. A single-centre, single blinded, randomised controlled study. Single university hospital, from May to July 2019. Seventy-two healthy fasted volunteers were randomly allocated to different fluid volume groups. Participants randomly ingested predetermined volumes between 50 and 400 ml of a feeding-drink (Nutricia Nutridrink). Following a standardised gastric ultrasound scanning protocol, a blinded sonographer measured the antral cross-sectional area in the supine and right-lateral decubitus positions. Measurements were performed at baseline, 5 min postingestion and 20 min postingestion. Gastric volumes were predicted using the previously established Perlas model and compared with total gastric fluid volumes after ingestion of the study drink. The Perlas model underestimated the volume of thick gastric fluid and yielded a suboptimal fit for our data. However, antral cross-sectional area and total gastric thick fluid volumes were significantly correlated (Pearson's correlation coefficient 0.73, P < 0.01). A new model was fitted to predict gastric volumes of thick fluids, using the antral cross-sectional area (cm2) in the right-lateral decubitus position: Volume (ml) = 79.38 + 13.32 x right-lateral cross-sectional area. The Perlas model for clear gastric fluid volume estimation is suboptimal for thick fluid volume assessment and an alternative model is presented. Netherlands Trial Register Trial NL7677, Registration date: 16 April 2019; https://www.trialregister.nl/trial/7677.

Looking through the eyes of the multidisciplinary team: the design and clinical evaluation of a decision support system for lung cancer care.

BackgroundDecision-making in lung cancer is complex due to a rapidly increasing amount of diagnostic data and treatment options. The need for timely and accurate diagnosis and delivery of care demands high-quality multidisciplinary team (MDT) collaboration and coordination. Clinical decision support systems (CDSSs) can potentially support MDTs in constructing a shared mental model of a patient case. This enables the team to assess the strength and completeness of collected diagnostic data, stratification for the right personalized therapy driven by clinical stage and other treatment-influencing factors, and adapt care management strategies when needed. Current CDSSs often have a suboptimal fit into the decision-making workflow, which hampers their impact in clinical practice.MethodsA CDSS for multidisciplinary decision-making in lung cancer was designed to support the abovementioned goals through presentation of relevant clinical data in line with existing mental model structures of the MDT members. The CDSS was tested in a simulated multidisciplinary tumor board meeting for primary diagnosis and treatment selection, based on de-identified primary lung cancer cases (n=8). Decision course analysis, eye-tracking data and questionnaires were used to assess the impact of the CDSS on constructing shared mental models to improve the decision-making process and outcome.ResultsThe CDSS supported the team in their self-correcting capacity for accurate diagnosis and TNM classification. It enabled cross-validation of diagnostic findings, surfaced discordance between diagnostic tests and facilitated cancer staging according the diagnostic evidence, as well as spotting contra-indications for personalized treatment selection.ConclusionsThis study shows the potential of CDSS on clinical decision making, when these systems are properly designed in line with clinical thinking. The presented setup enables assessment of the impact of CDSS design on clinical decision making and optimization of CDSSs to maximize their effect on decision quality and confidence.

Reexamination of the psychometric properties of the Compensatory Eating and Behaviors in Response to Alcohol Consumption Scale (CEBRACS) and exploration of alternative scoring

ObjectiveThe primary aims of this paper were to reexamine the factor structure of the 21-item Compensatory Eating and Behaviors in Response to Alcohol Consumption Scale (CEBRACS), a measure of Food and Alcohol Disturbance (FAD), and investigate an alternative scoring structure. FAD is the use of disordered eating behaviors within an alcohol use episode to compensate for alcohol-related calories and/or increase effects of alcohol. The new scoring approach captures FAD behaviors based on whether they occurred before, during, or after alcohol use. MethodParticipants were 586 young adults (18–30 years; 77.6% female; 55.8% non-Hispanic White) who completed online questionnaires on alcohol use, disordered eating behaviors, and FAD. ResultsConfirmatory factor analyses did not provide unequivocal evidence for any previously proposed factor structures of the CEBRACS. Exploratory factor analysis suggested items capturing FAD “Before” drinking had a two-factor structure (i.e., Alcohol Effects and Compensatory Behaviors), items capturing FAD “During” drinking had a three-factor structure (i.e., Alcohol Effects, Diet & Exercise, and Extreme Weight Control Behaviors [EWCB]), and items capturing FAD “After” drinking had a two-factor structure (i.e., Diet & Exercise & EWCB). DiscussionThe CEBRACS factor structure is not consistent across samples; however, despite suboptimal fit, the original structure remains the best approach to capture FAD behaviors. The alternative scoring reflects FAD behaviors based on when they occur relative to alcohol use. The two scoring approaches provide researchers and clinicians flexibility to describe FAD behaviors in multiple ways using the CEBRACS.