Quantitative Measurements Research Articles

Computed tomography three-dimensional reconstruction in the diagnosis of bleeding small intestinal polyps: A case report

BACKGROUND Computed tomography (CT) small bowel three-dimensional (3D) reconstruction is a powerful tool for the diagnosis of small bowel disease and can clearly show the intestinal lumen and wall as well as the outside structure of the wall. The horizontal axis position can show the best adjacent intestinal tube and the lesion between the intestinal tubes, while the coronal position can show the overall view of the small bowel. The ileal end of the localization of the display of excellent, and easy to quantitative measurement of the affected intestinal segments, the sagittal position for the rectum and the pre-sacral lesions show the best, for the discovery of fistulae is also helpful. Sagittal view can show rectal and presacral lesions and is useful for fistula detection. It is suitable for the assessment of inflammatory bowel disease, such as assessment of disease severity and diagnosis and differential diagnosis of the small bowel and mesenteric space-occupying lesions as well as the judgment of small bowel obstruction points. CASE SUMMARY Bleeding caused by small intestinal polyps is often difficult to diagnose in clinical practice. This study reports a 29-year-old male patient who was admitted to the hospital with black stool and abdominal pain for 3 months. Using the combination of CT-3D reconstruction and capsule endoscopy, the condition was diagnosed correctly, and the polyps were removed using single-balloon enteroscopy-endoscopic retrograde cholangiopancreatography without postoperative complications. CONCLUSION The role of CT-3D in gastrointestinal diseases was confirmed. CT-3D can assist in the diagnosis and treatment of gastrointestinal diseases in combination with capsule endoscopy and small intestinal microscopy.

Rates and predictors of reimplantation of matrix-induced autologous chondrocyte implantation following first stage cartilage harvest: A cohort study

PurposeTo assess the reimplantation rate and predictors of patients requiring second-staged matrix-induced autologous chondrocyte implantation (MACI) reimplantation after initial first stage cartilage biopsy. MethodsA retrospective review was performed from 2018 to 2022 among patients who underwent only phase I MACI biopsy procedure (biopsy group) or both phase I with transition to phase II implantation of chondrocytes (implantation group) at a single tertiary center. Demographic, qualitative, and quantitative measurements were recorded, and univariate and multivariate regression analysis was performed to assess predictors of ultimately requiring second stage MACI implantation. ResultsA total of 71 patients (51% female, age 27.7 ± 10.6 years (range 12–50)) were included in this study. Eventually, 25 of 71 patients (35.2%) experienced persistence of symptoms after initial MACI biopsy and other concomitant procedures, requiring second-stage implantation. Univariate analysis showed the implantation group compared to the biopsy group had a greater lesion size (5.2 cm2 ± 3.3 vs. 3.3 cm2 ± 1.4, p = 0.024), a higher proportion patients ≥ 26 years of age (76% vs. 43%, p = 0.009), a medial femoral condyle lesion more commonly (33% vs 11%, p = 0.005), were more often female (72% vs. 39%, p = 0.008), and had less often soft tissue repair at time of biopsy (32% vs. 61%, p = 0.020). Backward multivariate logistic regression analysis revealed that size of the lesion (OR 1.43, p = 0.031) and age ≥ 26 years old at time of biopsy (OR 3.55, p = 0.042) were independent predictors of not responding to initial surgery and requiring implantation surgery. ConclusionThis study found that 35% of patients undergoing MACI phase I biopsy harvest eventually required autologous implantation. Independent risk factors for progressing to implantation after failed initial surgery were larger defect size and older age.Level of Evidence: III, Cohort Study.

The Use of Simulated Participant and Virtual Reality Simulation to Enhance Nursing Students’ Communication Skills in “End of Life Care” - A Single-Arm Repeated Measures Study

BackgroundIncreasing demand for palliative care necessitates effective training for nursing students in end-of-life (EOL) and advanced care planning (ACP). Conventional teaching methods often fall short. MethodsA single-arm pre-post design involved 219 nursing students in VR and SP simulations. Quantitative measures and qualitative feedback were obtained. ResultsStatistical analysis revealed significant improvement post-intervention (p < 0.001). Qualitative feedback highlighted realism, interactivity, and skill development. Challenges included emotional intensity and scenario repetition. ConclusionsCombining virtual reality (VR) and simulated participant (SP) simulation significantly enhances nursing students' confidence and communication for end-of-life (EOL) conversations, offering a realistic educational experience. Further research is recommended in understanding this teaching method.

Development of molecularly imprinted photonic crystal hydrogel based smart sensor for selective uric acid detection

Determination of uric acid (UA) concentration is important in clinical analysis, as its variation is an indication of several health-related conditions. Here, we discuss the development of a Molecularly imprinted photonic crystal hydrogel (MICPH)-based colourimetric sensor for the selective detection of UA. The developed MIPCH-UA sensor displays a bright structural colour, which varies as the sensor swells upon rebinding the UA molecules. The structural colour change is directly related to the concentration of the target molecule and can serve as an easy readout of the sensor response. This colour change can also be recorded spectrally, providing a quantitative measure of the sensor response. With a low limit of detection (LoD) of 1.01×10-9M, the sensor is capable of detecting low concentrations of UA. Moreover, the sensor endows the advantage of fast responsiveness and reusability when employed for UA assays. The sensor performance remains consistently reliable over an extended duration of up to one month, thereby ensuring cost-effectiveness in monitoring UA levels. The specific detection capability of the sensor allows the accurate and reliable measurement of UA in the presence of various interfering molecules and complex sample matrices. The structural colour change of the sensor was used to train a CNN-based deep learning algorithm to quantitatively predict the UA concentration. The integration of the machine learning algorithm with the developed sensor provides an accurate and efficient smart sensor platform well-suited for real-time sensing of uric acid.

Comparison of DNA quantity and quality from fecal samples of mammals transported in ethanol or lysis buffer

Using fecal microbial community profiles through sequencing approaches helps to unravel the intimate interplay between health, wellness, and diet in wild animals with their environment. Ensuring the proper preservation of fecal samples before processing is crucial to ensure reliable results. In this study, we evaluated the efficiency of two different preservation methods, considering the following criteria: DNA yield, quality and integrity, and microbial community structure based on Oxford Nanopore amplicon sequencing of the V3-V4 region of bacterial 16 s rRNA and protozoa 18S rRNA genes. Eighteen matched pairs of mammalian fecal samples were collected and transported in 99.8% ethanol and lysis buffer; processing occurred between 55 and 461 days post-collection. Wilcoxon signed-rank tests were used to analyze quantitative measurements for paired samples. The A260/280 ratio, a measure of nucleic acid purity, was assessed descriptively for each media, and the Bartlett test evaluated dispersion of this ratio. A Fisher test was performed to compare the number of positive reactions for DNA extraction or PCR amplification of the 16S and 18S rRNA genes between both media. The concentration of total DNA and amplicons, as well as the number of reads obtained in sequencing, was significantly higher in the samples preserved with lysis buffer compared to ethanol, with magnitudes up to three times higher. Electrophoretic analysis of total DNA and amplicons further confirmed superior DNA integrity in lysis buffer preserved samples. The A260/280 values obtained using the lysis buffer were of optimal purity (mean: 1.92) and with little dispersion (SD: 0.27); on the other hand, the ethanol samples also presented an excellent average quality (mean: 1.94), but they were dispersed (SD: 1.10). For molecular studies using mammalian feces, the lysis Buffer reagent proved to be a reliable solution for their collection, conservation, and storage.

Optimization of the fluorogen-activating protein tag for quantitative protein trafficking and co-localization studies in S. cerevisiae.

Spatial and temporal tracking of fluorescent proteins in live cells permits visualization of proteome remodeling in response to extracellular cues. Historically, protein dynamics during trafficking have been visualized using constitutively active fluorescent proteins (FPs) fused to proteins of interest. While powerful, such FPs label all cellular pools of a protein, potentially masking the dynamics of select subpopulations. To help study protein subpopulations, bioconjugate tags, including the fluorogen activation proteins (FAPs), were developed. FAPs are comprised of two components: a single-chain antibody (SCA) fused to the protein of interest and a malachite-green (MG) derivative, which fluoresces only when bound to the SCA. Importantly, the MG derivatives can be either cell-permeant or -impermeant, thus permitting isolated detection of SCA-tagged proteins at the cell surface and facilitating quantitative endocytic measures. To expand FAP use in yeast, we optimized the SCA for yeast expression, created FAP-tagging plasmids, and generated FAP-tagged organelle markers. To demonstrate FAP efficacy, we coupled the SCA to the yeast G-protein coupled receptor Ste3. We measured Ste3 endocytic dynamics in response to pheromone and characterized cis- and trans-acting regulators of Ste3. Our work significantly expands FAP technology for varied applications in S. cerevisiae.

Digital "flipbooks" for enhanced visual assessment of simple and complex brain tumors.

Typical longitudinal radiographic assessment of brain tumors relies on side-by-side qualitative visualization of serial magnetic resonance images (MRIs) aided by quantitative measurements of tumor size. However, when assessing slowly-growing tumors and/or complex tumors, side-by-side visualization and quantification may be difficult or unreliable. Whole-brain, patient-specific "digital flipbooks" of longitudinal scans are a potential method to augment radiographic side-by-side reads in clinical settings by enhancing the visual perception of changes in tumor size, mass effect, and infiltration across multiple slices over time. In this approach, co-registered, consecutive MRI scans are displayed in a slide deck, where one slide displays multiple brain slices of a single timepoint in an array (e.g. 3x5 "mosaic" view of slices). The flipbooks are viewed similar to an animated flipbook of cartoons/photos so that subtle radiographic changes are visualized via perceived motion when scrolling through the slides. Importantly, flipbooks can be created easily with free, open-source software. This article describes the step-by-step methodology for creating flipbooks and discusses clinical scenarios for which flipbooks are particularly useful. Example flipbooks are provided in the Online Supplemental Material.

Shear wave elastography for solid breast masses evaluation: Quantitative measurement of mean elasticity value and elasticity ratio

PurposeShear wave elastography (SWE), an ultrasonographic technique to measure the elasticity of mass lesions to evaluate breast mass. This study aimed to find out the cutoff values identifying breast malignancy using the mean elasticity (E-mean) and elasticity ratio (E-ratio) of breast masses. MethodsThis retrospective study included women underwent SWE and US-guided biopsy of breast masses. During conventional US, the SWE mode was also performed, determining elasticity measurements, E-mean and E-ratio. Histopathological reports were obtained to identify mass status. The optimal and alternative cutoff values for E-mean and E-ratio to determine malignancy were assessed by receiver operating characteristic (ROC) curve analysis and Youden’s index score. ResultsAmong 147 benign and 93 malignant masses, the median of E-means were 26.20 (IQR 15.70–56.60) and 141.60 (IQR 119.80–154.60) kPa and the median E-ratios were 3.11 (IQR 1.83–5.23) and 9.24 (IQR 6.76–12.44), respectively. Using Youden’s index, the optimal cutoff values for E-mean and E-ratio were 90.35 and 5.89, with sensitivity of 87.1 % and 82.8 %, specificity of 89.1 % and 83.7 %, positive predictive value (PPV) of 83.5 % and 76.2 %, negative predictive value (NPV) of 91.6 % and 88.5 %, positive likelihood ratio (LR+) of 8.00 and 5.07, and negative likelihood ratio (LR-) of 0.14 and 0.21, respectively. ConclusionThis study revealed that SWE is useful in predicting malignancy. With the optimal cutoff values of E-mean and E-ratio at 90.35 kPa and 5.89, the sensitivity was nearly 90 % with E-mean and slightly over 80 % with E-ratio, respectively. These findings could be used in conjunction with conventional US.

Limiting environmental reporting flexibility: investor judgment based on the EU taxonomy

AbstractThis experimental research investigates the effect of different types of environmental information on investor judgment. By examining three experimental cases varying the level of environmental disclosure, we evaluate the investment judgments of professional (Study 1) and private German investors (Study 2). Primarily, we investigate whether traditional, commonly disclosed environmental information affects investor judgments. Furthermore, we explore the effects of linking non-financial reporting elements to quantitative financial measures through the EU taxonomy by adding taxonomy indicators. Specifically, we operationalized the case where companies fall into a category of poor environmental performance by taxonomy classification. We find that only traditional environmental disclosure in combination with standardized taxonomy-aligned information (below average), influences the investment judgment. However, professional investors exhibit a significantly negative response, while private investors show a significantly positive reaction when constraining reporting flexibility through the inclusion of standardized taxonomy measures with poor performance. Consequently, we conclude that the connection between non-financial reporting elements and quantitative standardized financial measures enhances transparency for professional investors. Private investors, on the other hand, reward additional taxonomy-aligned environmental information irrespective of its content. This implies that environmental information generally conveys positive signals to private investors, but uncertainty in investment judgment can be assumed.

Scientific text citation analysis using CNN features and ensemble learning model.

Citation illustrates the link between citing and cited documents. Different aspects of achievements like the journal's impact factor, author's ranking, and peers' judgment are analyzed using citations. However, citations are given the same weight for determining these important metrics. However academics contend that not all citations can ever have equal weight. Predominantly, such rankings are based on quantitative measures and the qualitative aspect is completely ignored. For a fair evaluation, qualitative evaluation of citations is needed in addition to quantitative ones. Many existing works that use qualitative evaluation consider binary class and categorize citations as important or unimportant. This study considers multi-class tasks for citation sentiments on imbalanced data and presents a novel framework for sentiment analysis in in-text citations of research articles. In the proposed technique, features are retrieved using a convolutional neural network (CNN), and classification is performed using a voting classifier that combines Logistic Regression (LR) and Stochastic Gradient Descent (SGD). The class imbalance problem is handled by the synthetic minority oversampling technique (SMOTE). Extensive experiments are performed in comparison with the proposed approach using SMOTE-generated data and machine learning models by term frequency (TF), and term frequency-inverse document frequency (TF-IDF) to evaluate the efficacy of the proposed approach for citation analysis. It is found that the proposed voting classifier using CNN features achieves an accuracy, precision, recall, and F1 score of 0.99 for all. This work not only advances the field of sentiment analysis in academic citations but also underscores the importance of incorporating qualitative aspects in evaluating the impact and sentiments conveyed through citations.

Advanced MRI techniques in abdominal imaging.

Magnetic resonance imaging (MRI) is a crucial modality for abdominal imaging evaluation of focal lesions and tissue properties. However, several obstacles, such as prolonged scan times, limitations in patients' breath-hold capacity, and contrast agent-associated artifacts, remain in abdominal MR images. Recent techniques, including parallel imaging, three-dimensional acquisition, compressed sensing, and deep learning, have been developed to reduce the scan time while ensuring acceptable image quality or to achieve higher resolution without extending the scan duration. Quantitative measurements using MRI techniques enable the noninvasive evaluation of specific materials. A comprehensive understanding of these advanced techniques is essential for accurate interpretation of MRI sequences. Herein, we therefore review advanced abdominal MRI techniques.

Astragalus Polysaccharide Enhances Voriconazole Metabolism under Inflammatory Conditions through the Gut Microbiota.

Voriconazole (VRC), a widely used antifungal drug, often causes hepatotoxicity, which presents a significant clinical challenge. Previous studies demonstrated that Astragalus polysaccharide (APS) can regulate VRC metabolism, thereby potentially mitigating its hepatotoxic effects. In this study, we aimed to explore the mechanism by which APS regulates VRC metabolism. First, we assessed the association of abnormal VRC metabolism with hepatotoxicity using the Roussel Uclaf Causality Assessment Method scale. Second, we conducted a series of basic experiments to verify the promotive effect of APS on VRC metabolism. Various in vitro and in vivo assays, including cytokine profiling, immunohistochemistry, quantitative polymerase chain reaction, metabolite analysis, and drug concentration measurements, were performed using a lipopolysaccharide-induced rat inflammation model. Finally, experiments such as intestinal biodiversity analysis, intestinal clearance assessments, and Bifidobacterium bifidum replenishment were performed to examine the ability of B. bifidum to regulate the expression of the VRC-metabolizing enzyme CYP2C19 through the gut-liver axis. The results indicated that APS does not have a direct effect on hepatocytes. However, the assessment of gut microbiota function revealed that APS significantly increases the abundance of B. bifidum, which could lead to an anti-inflammatory response in the liver and indirectly enhance VRC metabolism. The dual-luciferase reporter gene assay revealed that APS can hinder the secretion of pro-inflammatory mediators and reduce the inhibitory effect on CYP2C19 transcription through the nuclear factor-κB signaling pathway. The study offers valuable insights into the mechanism by which APS alleviates VRC-induced liver damage, highlighting its immunomodulatory influence on hepatic tissues and its indirect regulatory control of VRC-metabolizing enzymes within hepatocytes.

SUSTAINABLE CONSTRUCTION MANAGEMENT SYSTEM

This paper presents a systematic approach to environmental management of pollution and/or hazards caused by construction projects. It proposes a qualitative approach to assess and control the problem and a method to calculate the Construction Pollution Index (CPI) which provides a quantitative measurement of pollution and/or hazards caused by the construction projects. Questionnaires were distributed to the organization and the results were made into percentage, level of readiness index and regression test to analyze the organizations responds. Results was analyzed through Statistical Package for the Social Sciences tools Environmental impact assessments (EIA) and environmental management systems (EMS) enable organizations to identify, access and manage environmental impacts throughout a development's lifecycle, improving overall environmental performance. Based on the analysis and discussions, the paper further proposes that major construction companies should obtain ISO 14001 Environmental Management System (EMS) certifications. Keywords: environmental management, hazards, Environmental impact assessments (EIA), environmental management systems (EMS).

Quantified Metrics of Gastric Emptying Delay by Glucagon-Like Peptide-1 Agonists: A Systematic Review and Meta-Analysis With Insights for Periprocedural Management.

Divergent recommendations for periprocedural management of glucagon-like peptide-1 (GLP-1) receptor agonist (GLP-1 RA) medications rely on limited evidence. We performed a systematic review and meta-analysis to provide quantitative measures of gastric emptying relevant to mechanisms of weight loss and to periprocedural management of GLP-1 RA. We hypothesized that the magnitude of gastric emptying delay would be low and of limited clinical significance to procedural sedation risks. A protocolized search identified studies on GLP-1 RA that quantified gastric emptying measures. Pooled estimates using random effects were presented as a weighted mean difference with 95% confidence intervals (CIs). Univariate meta-regression was performed to assess the influence of GLP-1 RA type, short-acting vs long-acting mechanism of action, and duration of treatment on gastric emptying. Fifteen studies met the inclusion criteria. Five studies (n = 247) utilized gastric emptying scintigraphy. Mean T 1/2 was 138.4 minutes (95% CI 74.5-202.3) for GLP-1 RA vs 95.0 minutes (95% CI 54.9-135.0) for placebo, with a pooled mean difference of 36.0 minutes (95% CI 17.0-55.0, P < 0.01, I2 = 79.4%). Ten studies (n = 411) utilized the acetaminophen absorption test, with no significant delay in gastric emptying measured by T max , area under the curve (AUC) 4hr , and AUC 5hr with GLP-1 RA ( P > 0.05). On meta-regression, the type of GLP-1 RA, mechanism of action, and treatment duration did not impact gastric emptying ( P > 0.05). While a gastric emptying delay of ∼36 minutes is quantifiable on GLP-1 RA medications, it is of limited magnitude relative to standard periprocedural fasting periods. There were no substantial differences in gastric emptying on modalities reflective of liquid emptying (acetaminophen absorption test), particularly at time points relevant to periprocedural care.

COVID Pneumonia Severity Detection of Chest CT-Scan Images based on Robust Semantic Segmentation

Image segmentation has steadily grown especially for clinical usage and disease recognition in radiological research. This procedure, aimed at acquiring quantitative measurements, strives to distinguish regions or objects of interest from adjacent body tissues. To be more specific, it entails measuring the area and volume of segmented structures to extract more refined diagnostic information. The main hurdles encountered by segmentation algorithms originate from challenges like variations in intensity, artifacts, and the close juxtaposition of diverse soft tissues in the grayscale. In this paper, a robust semantic segmentation is proposed to specify the infected regions of lung images and consider the severity degree of the pneumonia caused by COVID-19 disease. The proposed model provides an accurate diagnosis of the chest CT scan image with satisfied performance with 93% accuracy and the second most important metric which is the Jaccard Index with 0.746±0.09 shows higher prediction performance than most existing systems in the literature.

Ensuring a sustainable and healthy primary school of the future: Finding answers through group concept mapping

Objectives: Overweight and obesity among school-aged children pose a threat to both their academic performance and public health. The Healthy Primary School of the Future (HPSF) initiative was established to address this issue. Our objective was to explore the conditions that make HPSF sustainable based on the perspectives of relevant stakeholders. Design: The study utilised Group Concept Mapping, a structured methodology for conducting mixed-methods participative research, combining qualitative data collection with quantitative data analysis measures. Method: Participants included parents, teachers, school directors, politicians, labour unions, educational, nutrition and health scientists and policymakers. They were asked to respond to the prompt, ‘A necessary condition to make the HPSF sustainable is . . .’. The statements generated were then assessed for their importance and feasibility. Using multidimensional scaling and hierarchical cluster analyses, we identified the shared vision among the stakeholders. Results: A total of 106 unique statements were generated and grouped statistically into 10 clusters. The most significant clusters were identified as Financing; Accessibility for everyone; and Content/Lifestyle/School Programme. The clusters that were deemed most feasible included Content/Lifestyle/School Programme; Accessibility for everyone; and Evaluation. Conclusion: Achieving sustainability for the HPSF requires a comprehensive approach that addresses the conditions outlined in all 10 clusters. Based on the ratings of feasibility and importance, our recommendation is to prioritise implementation of Content/Lifestyle/School Programme and Accessibility for everyone. Subsequently, efforts should be directed towards realising the less feasible but crucial conditions, such as Financing and Evaluation, followed by the remaining six clusters of conditions as identified.

Comparison of quantitative and qualitative anti-dsDNA assays.

In evaluation of systemic lupus erythematosus (SLE), anti-double-stranded DNA antibodies (anti-dsDNA) play a significant role in diagnosis, monitoring SLE activity, and assessing prognosis. However, evaluations of the performance and limitations for recently developed methods for anti-dsDNA assessment are sparse. Specimens used for antinuclear antibody testing (n = 129) were evaluated for anti-dsDNA assay comparability across 4 medical centers in the United States. The methods compared were Werfen Quanta Lite dsDNA, Zeus Scientific dsDNA Enzyme Immunoassay, Bio-Rad multiplex immunoassay (MIA) dsDNA, ImmunoConcepts Crithidia, and Bio-Rad Laboratories Crithidia. For quantitative anti-dsDNA measurements, Spearman's correlation coefficient was highest between Zeus and Werfen (ρ = 0.86; CI, 0.81-0.90; P < .0001). Comparison of MIA to Werfen or Zeus yielded similar results to each other (ρ = 0.58; CI, 0.44-0.68; P < .0001; and ρ = 0.59; CI, 0.46-0.69; P < .0001, respectively), but lower than the correlation between Zeus and Werfen. Positive concordance between assays ranged from 31.4% to 97.1%, and negative concordance between assays ranged from 58.5% to 100%. The detection of anti-dsDNA in those with SLE diagnosis ranged from 50.9% to 77.4% for quantitative assays and 15.1% to 24.5% for Crithidia assays. Current quantitative anti-dsDNA assays are not interchangeable for patient follow-up. Crithidia-based assays demonstrate high negative concordance and lack positive concordance among the methods.

The Nolan Index: An R Program for Calculating List Resemblances

The Nolan Index (NI) is a normed, quantitative measure for comparing the degree of resemblance (similarity or dissimilarity) between free listings with an Excel program for calculating it. This article enhances that effort with the addition of an R program and additional applications. Free-list resemblance measures have been used to investigate and compare historical and cultural connections, ecological relationships, cultural item utilities, practices, knowledge, and values both within and between various groups. Their utility for cultural domain analysis and the theory of cognitive cultural models is also considered.

There is high variability in quantitative measurement techniques in glenohumeral capsular measurements for shoulder instability: A systematic review.

Instability of the glenohumeral joint remains a complex clinical issue with high rates of surgical failure and significant morbidity. Advances in specific radiologic measurements involving the glenoid and the humerus have provided insight into glenohumeral pathology, which can be corrected surgically towards improving patient outcomes. The contributions of capsular pathology to ongoing instability remain unclear. The purpose of this study is to provide a systematic review of existing glenohumeral capsular measurement techniques published in the last 15 years. A systematic review of multiple databases was performed following PRISMA guidelines for all primary research articles between 2008 and 2023 with quantitative measurements of the glenohumeral capsule in patients with instability, including anterior, posterior and multi-directional instability. There were a total of 14 articles meeting the inclusion criteria. High variability in measurement methodology across studies was observed, including variable amounts of intra-articular contrast, heterogeneity among magnetic resonance sequence acquisitions, differences in measurements performed and the specific approach taken to compute each measurement. There is a need for standardization of methods in the measurement of glenohumeral capsular pathology in the setting of glenohumeral instability to allow for cross-study analysis. Level III.

Toxic positivity intentions: an image management approach to upward social comparison and false self-presentation

Abstract Media users are often overwhelmed with excessive amounts of positivity on social media. While a healthy positive outlook acknowledges both the positive and negative aspects of life, a growing term in popular media referring to toxic positivity suggests users leverage positivity and avoid negativity for personal gain. Employing a two-study, multi-method design, Study 1 is framed within the impression management literature to conceptually and operationally define perceived toxic positivity intentions (TPI) through qualitative and quantitative measures. Study 2 is framed within the bandwagon and descriptive norm literature to investigate the role of perceived TPI in upward social comparisons and digital self-presentation behaviors. Results of Study 1 indicate a five-factor solution of perceived TPI on social media. Results of Study 2 suggest the relationship between (most) dimensions of perceived TPI and false social media self-presentations is significantly mediated by upward social comparison.