Diagnostic Labels Research Articles

Abstract 4119052: Redefining the phenotypic continuum of cardiovascular disease using machine learning

Background: Current approaches that define cardiovascular disease rely on assignment of traditional diagnostic labels which may not reflect the natural continuum of phenotypic expression, influenced by genetic and environmental modifiers, or adequately identify groups with shared molecular mechanisms. Machine learning can leverage advances in genetic and imaging characterisation to reclassify phenotypic diversity along a continuum, from health to disease. Research Aims: Our study aims to build a tree-like classification of cardiovascular phenotypes in the community where branches represent subjects with shared features, ordered by their severity. Using dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) as exemplar conditions with opposing phenotypes, we aim to demonstrate the interaction between genetic and environmental modifiers of disease expression. Methods: We analysed cardiovascular magnetic resonance (CMR) imaging, electrocardiogram (ECG), biomarkers and clinical data from participants in the UK Biobank. Using unsupervised learning of multiparametric data, we built a tree of phenotypic expression. We projected the risk of DCM and HCM cases, and associated rare and common variant risk, onto the tree structure. Results: Ten main branches were discovered, using data from 41,525 participants (51.7 % female, median age 65 yrs [IQR: 58, 70]). The extremities of branches 1 and 6 were enriched for DCM cases (p <0.05) and participants with reduced ejection fraction and high left ventricular volumes (p <0.05). Compared with subjects at the tree centre, distal participants in branch 1 had increased blood pressure (BP) and body mass index (BMI), whilst those in branch 6 were more likely to have high HbA1c levels and carry a pathogenic DCM variant (p <0.05). The extremity of branch 5 was enriched for HCM cases, rare sarcomeric variants (p = 0.004) and high polygenic risk (p <0.001). Whilst the ends of branches 3 and 9 had high polygenic risk for HCM, they were less likely to have elevated BP and BMI, and had reduced likelihood of HCM expression, compared with branch 5. A phenomapping model was trained to accurately project unseen subjects onto the tree (R 2 0.98). Conclusions: We present a tree-like continuum of cardiovascular phenotypes, providing a novel framework for mechanistic discovery and exploration of genotype-phenotype associations. Phenomapping of unseen subjects enables personalised estimation of genetic and cardiovascular risk.

Abstract 4125281: Detection of Acute Myocardial Infarction and Ischemia From Lead-I ECG Using Deep Learning

Introduction: While smartwatches that acquire Lead-I electrocardiogram (ECG) have shown promise in detecting arrhythmias, the detection of ischemic events using Lead-I alone remains understudied. We aim to build and evaluate a deep-learning model to determine whether Lead-I ECG can identify ischemia/infarction in a 12-lead ECG. Methods: We developed a deep-learning model that can identify MI/ischemia using only Lead-I ECG signal as input. For this task, we utilized PTB-XL, a large publicly available dataset of 12-Lead clinical ECG with associated diagnostic labels. Our experiment utilized 8781 ECGs whose labels were validated by at-least one cardiologist with 100% confidence scores. This dataset includes 1391 ECGs that are labeled as either ischemic ST/T changes or MI, and 7390 ECGs with either “normal” labels or non-ischemic ST/T abnormalities. We split the dataset in training, validation, and holdout sets (80-10-10) with equal stratification of the diagnosis classes. The holdout test set contains 909 non-ischemic ECGs and 233 ECGs with MI/ischemia diagnosis, 106 of which are labeled as acute MI or ischemic ST/T changes and 127 labeled as other MI chronicity. The discriminatory ability of the model is evaluated with the Area Under the receiver-operating Curve (AUC), sensitivity, specificity, and the predictive values. We further analyze the model’s capability in discriminating the cohort of acute MI and ischemia diagnoses from other MI as well as from the non-ischemic cohort. Results: The model has an AUC of 0.88 for discriminating an MI/ischemia vs normal. Using a threshold corresponding to 80% sensitivity, the corresponding specificity is 79% and the negative and positive predictive values are 94% and 50% (Table-1). Although the model was trained to identify the combined class of MI/ischemia, it can differentiate acute MI and ischemia cohorts from other MI as well as from non-ischemic diagnoses with p-values<10 -4 (Table-2). Lastly, with the same threshold, the sensitivity for detecting acute inferior myocardial infarctions or inferior ischemia – a class that is difficult to detect with Lead-I alone – is 82%. Conclusions: Our deep learning model, which was trained on Lead-I ECG alone, can effectively detect acute MI and ischemia that would have been identified using a 12-lead ECG. This method presents a proof-of-concept for applications in home settings. Further prospective validation using wearable ECG monitors that record lead-I is warranted.

I am in Charge, But Not Always in Control: A Qualitative Exploration of Female's Beliefs and Knowledge About Their Pelvic Pain.

Beliefs and knowledge about pain can influence how an individual approaches pain management. At present, there have been no investigations into the beliefs and knowledge that females hold about their pelvic pain; therefore, pain intervention approaches may not be targeted at current patient understandings. Exploring beliefs and knowledge may help inform and subsequently improve pain intervention outcomes for pelvic pain by allowing more tailored content to be delivered. This study aimed to identify beliefs and knowledge that females hold about their pelvic pain. Semistructured interviews (n = 12) were conducted. Based on the common sense model, interview questions investigated participants' beliefs and knowledge related to the identity (diagnostic labels), cause, consequence, control, and timeline of their pelvic pain. Transcribed interviews were analyzed using an Interpretive Description Framework. Participants reported holding biopsychosocial understandings of pain; however, many described their own pain experience in a way that was more aligned with the biomedical model of pain, suggesting incongruence in pain understanding. Participants valued their ability to take charge of their pain management, highlighting bodily autonomy and being proactive in pain management approaches. This approach to management did not provide total control over participants' pelvic pain. Pelvic pain was perceived as disruptive of life, both at present and into the future, with the predictability of pain varying over time. This study provides insight into the perspectives of females with pelvic pain. Recommendations for clinicians are provided with a focus on delivering targeted education and equipping patients with helpful representations of their pain.

Psychiatric and functional correlates of stigma associated with cognitive impairment in schizophrenia

Schizophrenia is the most strongly stigmatized psychiatric diagnosis, with negative stereotypes including assumptions of incompetence and inability to recover. Individuals with cognitive impairment associated with schizophrenia (CIAS) have reported stigma experiences, suggesting that CIAS carries stigma in addition to the stigma associated with schizophrenia as a diagnostic label. While research has established that mental illness stigma more generally is linked with poor psychiatric and functional outcomes, no research has explored correlates of CIAS stigma. This study evaluated cognitive, psychiatric, and functional correlates of CIAS stigma among 54 individuals with schizophrenia spectrum disorders participating in a cognitive remediation trial. Participants with greater estimated cognitive decline reported higher levels of CIAS stigma experiences. Participants who reported higher levels of CIAS stigma also scored higher on a measure of depressive symptom severity. No significant associations were found between CIAS stigma and positive and negative psychosis symptoms or general psychopathology ratings. CIAS stigma was not associated with performance-based functional capacity or ratings of community functioning. Findings suggest that CIAS stigma is linked with the degree of cognitive decline and depressive symptom severity among individuals with schizophrenia spectrum disorders. Additional research is needed to elucidate directionality and the relationship between CIAS stigma and functioning outcomes.

A multicenter bladder cancer MRI dataset and baseline evaluation of federated learning in clinical application

Bladder cancer (BCa), as the most common malignant tumor of the urinary system, has received significant attention in research on the clinical application of artificial intelligence algorithms. Nevertheless, it has been observed that certain investigations use data from various medical facilities to train models for BCa, which may pose a privacy risk. Given this concern, protecting patient privacy during machine learning algorithm training is a crucial aspect that requires substantial attention. One emerging machine learning paradigm that addresses this concern is federated learning (FL). FL enables multiple entities to collaboratively build machine learning models while preserving data privacy and security. In this study, we present a multicenter BCa magnetic resonance imaging (MRI) dataset. The dataset comprises 275 three-dimensional bladder T2-weighted MRI scans collected from four medical centers, and each scan includes diagnostic pathological labels for muscle invasion and pixel-level annotations of tumor contours. Four FL methods are used to assess the baseline of the dataset for both the task of diagnosing muscle-invasive bladder cancer and automatic bladder tumor lesion segmentation.

Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations.

Psychiatric disorders are complex and influenced by both genetic and environmental factors. However, studying the full spectrum of these disorders is hindered by practical limitations on measuring human behavior. This highlights the need for novel technologies that can measure behavioral changes at an intermediate level between diagnosis and genotype. Wearable devices are a promising tool in precision medicine, since they can record physiological measurements over time in response to environmental stimuli and do so at low cost and minimal invasiveness. Here we analyzed wearable and genetic data from a cohort of the Adolescent Brain Cognitive Development study. We generated >250 wearable-derived features and used them as intermediate phenotypes in an interpretable AI modeling framework to assign risk scores and classify adolescents with psychiatric disorders. Our model identifies key physiological processes and leverages their temporal patterns to achieve a higher performance than has been previously possible. To investigate how these physiological processes relate to the underlying genetic architecture of psychiatric disorders, we also utilized these intermediate phenotypes in univariate and multivariate GWAS. We identified a total of 29 significant genetic loci and 52 psychiatric-associated genes, including ELFN1 and ADORA3. These results show that wearable-derived continuous features enable a more precise representation of psychiatric disorders and exhibit greater detection power compared to categorical diagnostic labels. In summary, we demonstrate how consumer wearable technology can facilitate dimensional approaches in precision psychiatry and uncover etiological linkages between behavior and genetics.

Influence of Different Diagnostic Criteria on Alzheimer Disease Clinical Research.

Updates in Alzheimer disease (AD) diagnostic guidelines by the National Institute on Aging-Alzheimer's Association (NIA-AA) and the International Working Group (IWG) over the past 11 years may affect clinical diagnoses. We assessed how these guidelines affect clinical AD diagnosis in a cohort of cognitively unimpaired (CU) and cognitively impaired (CI) individuals. We applied clinical and biomarker data in algorithms to classify individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort according to the following diagnostic guidelines for AD: 2011 NIA-AA, 2016 IWG-2, 2018 NIA-AA, and 2021 IWG-3, assigning the following generic diagnostic labels: (1) not AD (nAD), (2) increased risk of developing AD (irAD), and (3) AD. Diagnostic labels were compared according to their frequency, convergence across guidelines, biomarker profiles, and prognostic value. We also evaluated the diagnostic discordance among the criteria. A total of 1,195 individuals (mean age 73.2 ± 7.2 years, mean education 16.1 ± 2.7, 44.0% female) presented different repartitions of diagnostic labels according to the 2011 NIA-AA (nAD = 37.8%, irAD = 23.0%, AD = 39.2%), 2016 IWG-2 (nAD = 37.7%, irAD = 28.7%, AD = 33.6%), 2018 NIA-AA (nAD = 40.7%, irAD = 9.3%, AD = 50.0%), and 2021 IWG-3 (nAD = 51.2%, irAD = 8.4%, AD = 48.3%) frameworks. Discordant diagnoses across all guidelines were found in 512 participants (42.8%) (138 [91.4%] occurring in only β-amyloid [CU 65.4%, CI 34.6%] and 191 [78.6%] in only tau-positive [CU 71.7%, CI 28.3%] individuals). Differences in predicting cognitive impairment between nAD and irAD groups were observed with the 2011 NIA-AA (hazard ratio [HR] 2.21, 95% CI 1.34-3.65, p = 0.002), 2016 IWG-2 (HR 2.81, 95% CI 1.59-4.96, p < 0.000), and 2021 IWG-3 (HR 3.61, 95% CI 2.09-6.23, p < 0.000), but not with 2018 NIA-AA (HR 1.69, 95% CI 0.87-3.28, p = 0.115). Over 42% of the studied population presented discordant diagnoses when using the different examined AD criteria, mostly in individuals with a single positive biomarker. Except for 2018 NIA-AA, all guidelines identified asymptomatic individuals at risk of cognitive impairment. Our findings highlight important differences between the guidelines, emphasizing the necessity for updated criteria with enhanced staging metrics, considering clinical, research, therapeutic, and trial design aspects.

An Analysis of the Structures People With Shoulder Pain Recall After Receiving Their Diagnostic Imaging Results.

Rotator cuff related shoulder pain (RCRSP) is a prevalent cause of musculoskeletal pain. Patients presenting with this condition often undergo diagnostic imaging. However, many patients appear to have difficulty recalling the nature of their diagnosis. This may impact their rehabilitation outcomes. The aim of this study was to explore peoples' recall of their structural features relating to RCRSP by comparing their recalled description of their condition with the contents of their imaging reports as reported in an online questionnaire. In doing so, we aim to explore the potential emphasis they place on structural features related to their condition. This study was a content analysis embedded within a larger survey study published previously. Data containing patients' recall of their diagnosis was used from the study, and analysed using content analysis to quantify content in terms of codes and categories. The aims of frequency, completeness and accuracy of recall were then explored. The analysis identified eight categories of terminology either recalled by patients or reported in imaging reports. The most frequent categories in reports were tendinopathy (71%) and joint (67%), but only 17% and 36% of participant responses contained these codes, respectively. Participants' completeness of recall compared with their imaging report was 30% on average. In terms of accuracy, only 8 out of 95 participants recalled their diagnoses 100% correctly. This study indicates that patients have poor overall recall of their RCRSP-related structural features. We contend that participants may have been able to recall what they viewed to be the most prominent structural feature in their imaging findings. It may be important for clinicians to consider the impact of diagnostic labelling, and whether a patient's ability to recall such labelling may have an influence on their outcomes.

Genetic investigations in cerebral palsy.

The original description of cerebral palsy (CP) contained case histories suggesting that perinatal environmental stressors resulted in brain injury and neurodevelopmental disability. While there are clear associations between environmental impact on brain development and CP, recent studies indicate an 11% to 40% incidence of monogenic conditions in patients given a diagnosis of CP. A genetic diagnosis supports the delivery of personalized medicine. In this review, we describe how the Wnt pathway exemplifies our understanding of pathophysiology related to a gene variant (CTNNB1) found in some children diagnosed with CP. We cover studies undertaken to establish the baseline prevalence of monogenic conditions in populations attending CP clinics. We list factors indicating increased likelihood of a genomic diagnosis; and we highlight the need for a comprehensive, accurate, genotype-phenotype reference data set to aid variant interpretation in CP cohorts. We also consider the wider societal implications of genomic management of CP including significance of the diagnostic label, benefits and pitfalls of a genetic diagnosis, logistics, and cost.

Effects of diagnostic labels on perceptions of marginal cases of mental ill-health

Two experimental studies (Ns = 261, 684) investigated how diagnostic labels affect perceptions of people experiencing marginal levels of mental ill-health. These effects offer insight into the consequences of diagnostic “concept creep”, in which concepts of mental illness broaden to include less severe phenomena. The studies found consistent evidence that diagnostic labeling increases the perception that people experiencing marginal problems require professional treatment, and some evidence that it increases empathy towards them and support for affording them special allowances at work, school, and home. The studies also indicated that labels may reduce the control people are perceived to have over their problems and their likelihood of recovering from them. These findings point to the potential mixed blessings of broad diagnostic concepts and the cultural trends responsible for them. Expansive concepts may promote help-seeking, empathy, and support, but also undermine perceived agency and expectations that problems can be overcome.

Damaging dichotomies and confounding contradictions in mental health inpatient nursing: lessons learned from Orwell’s 1984

PurposeThis discursive paper considers the use of restrictive practices in mental health inpatient settings and how these are often prioritised over relational approaches, especially where the diagnostic label of personality disorder intersects with risk.Design/methodology/approachKey concepts from Orwell’s 1984 are studied for their pertinence to mental health inpatient settings, supporting our argument that restrictive practices arise from dichotomous thinking and externalised fears.FindingsDrawing upon Orwellian themes of power, social control and digital surveillance from 1984, the authors highlight the role of fear in perpetuating restrictive practices under a guise of benevolent care in mental health inpatient settings, especially for those who are diagnosed with a personality disorder. A lack of preparedness to work with complexity in such environments, coupled with a deficit in self-reflexivity and critical thinking, can exacerbate challenges.Research limitations/implicationsTo transcend damaging dichotomies and reduce restrictive practices in inpatient settings, the authors make the argument for the adequate preparation and education of the mental health nurse and authentic, collaborative, user-involved care.Originality/valueThe authors use Orwell’s novel to support a critical discourse around those damaging dichotomies and inherent contradictions that contribute to restrictive practice in contemporary mental health settings and to question whose interests’ these restrictive practices serve.

The Approach to Altered Mental Status in the Intensive Care Unit.

Altered mental status (AMS) is a syndrome posing substantial burden to patients in the intensive care unit (ICU) in both prevalence and intensity. Unfortunately, ICU patients are often diagnosed merely with syndromic labels, particularly the duo of toxic-metabolic encephalopathy (TME) and delirium. Before applying a nonspecific diagnostic label, every patient with AMS should be evaluated for specific, treatable diseases affecting the central nervous system. This review offers a structured approach to increase the probability of identifying specific causal etiologies of AMS in the critically ill. We provide tips for bedside assessment in the challenging ICU environment and review the role and yield of common neurodiagnostic procedures, including specialized bedside modalities of diagnostic utility in unstable patients. We briefly review two common etiologies of TME (uremic and septic encephalopathies), and then review a selection of high-yield toxicologic, neurologic, and infectious causes of AMS in the ICU, with an emphasis on those that require deliberate consideration as they elude routine screening. The final section lays out an approach to the various etiologies of AMS in the critically ill.

Revisiting Preaddiction

Abstract The directors of the National Institute on Drug Abuse and the National Institute on Alcohol Abuse and Alcoholism have proposed new efforts to enable earlier identification and intervention for harmful substance use and its consequences. As editors of The ASAM Principles of Addiction Medicine, we fully support this goal. The word “preaddiction” has been suggested as a diagnostic label to describe individuals who would be targeted for early intervention. In this commentary, we offer that “unhealthy substance use” would be a better descriptor than “preaddiction” and review several potential barriers to be addressed in order to maximize the impact of introducing this new paradigm.

Impact of the diagnostic label for a low-risk prostate lesion: protocol for two online factorial randomised experiments

IntroductionMany types of prostate cancer present minimal risk to a man’s lifespan or well-being, but existing terminology makes it difficult for men to distinguish these from high-risk prostate cancers. This...

Why Do Ionic Surfactants Significantly Alter the Chemiluminogenic Properties of Acridinium Salt?

Acridinium esters, due to their capability for chemiluminescence (CL), are employed as indicators and labels in biomedical diagnostics and other fields. In this work, the influence of ionic surfactants, hexadecyltrimethylammonium chloride and bromide (CTAC and CTAB, cationic) and sodium dodecyl sulphate (SDS, anionic) on the CL parameters and mechanism of representative emitter, 10-methyl-9-[(2-methylphenoxy)carbonyl]acridinium trifluoromethanesulphonate (2MeX) in a H2O2/NaOH environment, is studied. Our investigations revealed that the type of surfactant and its form in solution have an impact on the CL kinetic constants and integral efficiencies, while changes in those emission properties resulting from the type of ion (Cl- vs. Br-) are negligible. The major changes were recorded for systems containing surfactants at concentrations higher than the critical micelle concentration. The cationic surfactants (CTAC, CTAB) cause a substantial increase in CL emission kinetics and a moderate increase in its integral efficiency. At the same time, the opposite effect is observed in the case of SDS. Molecular dynamics simulations suggest that changes in emission parameters are likely due to differences in the binding strength of 2MeX substrate with surfactant molecules, which is higher for SDS than for CTAC. The results can help in rational designing of optimal acridinium CL systems and demonstrate their usefulness in distinguishing the pre- and post-micellar environment and the charge of surfactants.

Stigma of Binge-Eating Disorder in Hong Kong Chinese Adults: Effects of Weight Status, Diagnostic Labeling, and Etiological Explanations.

Binge-eating disorder (BED) is a strongly stigmatized condition and is often complicated by weight stigma. Research on the intersection between BED and weight stigma is scarce especially in Chinese populations. The present study examined BED stigma in Chinese, whether BED stigma was independent from weight stigma, and whether diagnostic labeling and etiological explanations influenced the degree of BED stigma. Using a between-subject experimental vignette study, 642 participants (mean age = 29.74 years, SD = 11.34) were randomly assigned to read one of the 18 vignettes, describing a character with information on BED symptoms, weight status, diagnostic labeling, and etiological explanations, followed by measures of stigma and help-seeking intentions. The character with BED symptoms was ascribed more negative personality characteristics, elicited more negative affective reactions, and triggered greater desired social distance compared to the character without BED symptoms. No evidence for weight stigma was found nor for its interaction with BED stigma. The Cantonese diagnostic label of BED, kwong sik zing, was associated with lower levels of volitional stigma and greater help-seeking intentions than the diagnostic label of eating disorders, jam sik sat tiu, and the absence of labeling. The effect of etiological explanations was only significant in the univariate test, indicating that providing either a psychosocial or a biogenetic etiological explanation lessened the negative evaluations of personality characteristics. The present study provided first evidence for BED stigma in Chinese. BED stigma appeared to be attributable to the presence of disordered eating behavior rather than the BED diagnosis.

The role of psychological formulation in inpatient settings in supporting staff empathy and therapeutic optimism for adults diagnosed with borderline personality disorder: A pre- and post-vignette study.

OBJECTIVES: National Health Service (NHS) values, such as empathy and therapeutic optimism, are integral when supporting service users with complex mental health presentations. There is some evidence to suggest that psychological formulation can increase empathy and optimism in healthcare professionals. This study, therefore, aimed to investigate whether a psychological formulation of a hypothetical service user with a complex presentation, typically labelled with a diagnosis of borderline personality disorder (BPD), increased empathy and therapeutic optimism in professionals working in mental health inpatient services. Sixty-six mental health professionals working in NHS inpatient services took part in a pre- and post-vignette study. Participants were asked to read a case vignette about a hypothetical service user, with a diagnostic label of BPD, and complete questionnaires capturing levels of empathy and therapeutic optimism. Participants were then randomized into two conditions and either asked to read the same information again (control condition) or read a psychological formulation based on the same hypothetical service user (intervention condition). The findings were analysed using a series of ANCOVAs/ANCOHETs. Two constructs of empathy (i.e. perspective taking and empathic concern), and therapeutic optimism significantly increased following exposure to the psychological formulation when compared to the control group condition. This study warrants further replication. These initial findings; however, indicate that psychological formulation can significantly increase the ability to perspective take, display empathic concern, and hold therapeutic optimism towards service users with a presentation associated with a diagnosis of BPD.

Amsel criteria based computer vision for diagnosing bacterial vaginosis

Bacterial vaginosis (BV) is a common vaginal infection that can predispose patients to several complications, such as pelvic inflammatory disease. Like many illnesses, existing diagnostic methods face a trade-off between diagnostic certainty and cost. To help address this dilemma, we explore a computational diagnostic approach implementable as an IoT device. We developed several deep learning models based on the Amsel criteria to evaluate different inexpensive point-of-care tests that better automate the diagnosis of BV. We first determined how to best diagnose BV via computer vision models trained on epithelial cell images. We found that training a ResNet18 model on NuSwab diagnostic labels achieved an 89% F1 score. We then experimented with augmenting computer vision results with other Amsel criteria values through multi-layer perceptrons, finding that also using whiff test values increased performance to an F1 of 91% and to a sensitivity surpassing human-performed Amsel criteria at 94.31%. These results provide the first insight into how combinations of images and other Amsel criteria data can best be used for reliable diagnoses, paving the way for future research into IoT-based BV diagnostics.

Borderline personality disorder stigma: Examining the effects of diagnostic disclosure, behavior, and gender as sources of stigma in the general population.

Borderline personality disorder (BPD) is a severe mental disorder characterized by a pervasive pattern of emotional and behavioral dysregulation. Dysfunction and distress may be compounded by stigmatizing beliefs held by members of the community. There is a lack of research focusing on stigmatizing beliefs about BPD held by the general population. This study addressed this gap by examining the relationship between BPD and a variety of stigma domains in a community sample. The current study explored whether (a) stigma is more strongly related to BPD symptomatic behavior or the diagnostic label of BPD, (b) attaching a diagnosis of BPD to symptomatic behavior or nonclinical behavior influences stigma, and (c) the gender of a vignette character influences the stigmatization of BPD. A total of 295 participants read vignettes and completed questionnaires assessing stigma type and intensity. Findings from the current study suggest that stigma is higher for BPD symptomatic behavior than for the diagnosis itself. Attaching a diagnostic label of BPD to BPD symptomatic behavior did not significantly impact the resultant stigma; however, the diagnosis was found to increase stigma for nonclinical behavior. Findings concerning BPD stigma and gender are in line with broader gender stereotypes. Specifically, there was greater pity for women displaying BPD behavior, whereas there was greater anger for men displaying the same behavior. BPD symptomatic behavior vignettes depicting a man also received a higher level of dangerousness and fear. Study limitations and future directions are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Failed back surgery syndrome-terminology, etiology, prevention, evaluation, and management: a narrative review.

Amid the worldwide increase in spinal surgery rates, a significant proportion of patients continue to experience refractory chronic pain, resulting in reduced quality of life and escalated healthcare demands. Failed back surgery syndrome (FBSS) is a clinical condition characterized by persistent or recurrent pain after one or more spinal surgeries. The diverse characteristics and stigmatizing descriptions of FBSS necessitate a reevaluation of its nomenclature to reflect its complexity more accurately. Accurate identification of the cause of FBSS is hampered by the complex nature of the syndrome and limitations of current diagnostic labels. Management requires a multidisciplinary approach that may include pharmacological treatment, physical therapy, psychological support, and interventional procedures, emphasizing realistic goal-setting and patient education. Further research is needed to increase our understanding, improve diagnostic accuracy, and develop more effective management strategies.