Diagnostic Conclusions Research Articles

A misleading miscellanea: a COVID-19 patient with fever and cytopenia

Complex clinical cases characterized by the coexistence of multiple pathologies and, consequently, multiple signs and symptoms shared by various pathological entities can hide pitfalls that may lead to incorrect diagnostic conclusions due to cognitive biases. Here, we present the case of a man who presents hyperpyrexia, progressive pancytopenia, and splenomegaly in the context of a SARS-CoV-2-related infection; the final diagnosis will be different from the initial diagnostic hypothesis.

The Association of Nasal and Blood Eosinophils with Serum IgE Level in Allergic Rhinitis and Asthma: A Case-Control Study.

Allergic rhinitis and asthma are two common respiratory diseases with allergic etiology in the world's population. Eosinophils and serum IgE levels have been known as inflammatory allergy markers for many years. This study aimed to evaluate the correlation of nasal and blood eosinophils with serum IgE levels in allergic rhinitis and asthma patients. This prospective study was done on patients (n = 78) diagnosed with asthma (n = 20), allergic rhinitis (n = 49), and chronic rhinosinusitis with nasal polyposis (CRSwNP) (n = 9) at our hospital in Ahvaz City, Iran. The age of participants in our study ranged from 3 to 73 years, and all of them were subjected to a complete blood count (CBC) test, nasal smear, and determination of serum IgE levels after their consent. There was no correlation between serum IgE level and nasal eosinophil count (p = 0.728) or between serum IgE level and blood eosinophil count (p = 0.657); however, a positive correlation was detected between blood and nasal eosinophil levels (p = 0.003). There is no significant relationship between serum IgE level and eosinophil count in the blood and nasal secretions. Serum IgE level and blood or nasal eosinophil count are both useful biomarkers for monitoring allergic rhinitis and asthma individually, but no diagnostic conclusion can be drawn from their correlation.

Визначення предикторів діагнозу пацієнтів з ПТСР на основі параметрів одновимірних моделей першого порядку BOLD-сигналів структур мозку та МГУА

Introduction. The use of functional magnetic resonance imaging (fMRI) allows for the assessment of processes occurring in the brain. By analyzing the examination results, it is possible to establish the parameters of connections between brain structures, and changes in the values of these parameters can be used as diagnostic conclusion predictors for PTSD-patients. Purpose. To identify predictors for the classification of the PTSD diagnosis using the connectivity parameters of BOLD signals from brain structures. Methods. The technology for identifying predictors of PTSD diagnosis is based on a) the formation of connectivity parameters of BOLD signals from brain structures obtained during resting-state scanning, b) the use of classifier-oriented selection based on inter-class variance and mRMR criteria to select informative features, and c) the classification of PTSD diagnosis using a logistic regression algorithm optimized by the Group Method of Data Handling. Results. The technology proposed in this work enabled the selection of informative features and the identification of their predictive forms, resulting in the formation of classifiers for the diagnosis of PTSD with high accuracy, sensitivity, and specificity. Conclusion. A technology for the formation, selection, and use of connectivity parameters of BOLD signals from brain structures has been proposed for differentiating healthy individuals from those who suffer with PTSD. A list of the most informative features of PTSD and their predictive forms in the form of generalized variables has been obtained, which can be used for diagnostic conclusions. The results obtained indicate the presence of a specific type of connection between the brain areas identified in the study based on levels of excitation (parameters а0 of the models) and the alteration of these levels in the context of PTSD.

Use of Telehealth in Autism Spectrum Disorder Assessment in Children: Evaluation of anOnline Diagnostic Protocol Includingthe Brief Observation of Symptoms of Autism.

The COVID-19 pandemic revealed the need to develop the field of remote assessment for autism spectrum disorders (ASD). The purpose of the study was to evaluate an online assessment protocol that includes the Brief Observation of Symptoms of Autism (BOSA). The online protocol consisting of BOSA and the Autism Diagnostic Interview-Revised (ADI-R) was administered by experienced examiners to 29 children with suspected ASD. The participants were then evaluated by clinical psychologists in a standard clinical setting using the Autism Diagnostic Observation Schedule-2 (ADOS-2) and other methods, and the ASD diagnosis was confirmed or ruled out. The results show substantial to moderate inter-rater agreement between the online and face-to-face raters with the value of Cohen's Kappa = 0.66 (P < 0.001); this corresponds with 79.8% agreement. The sensitivity of the protocol was approx. 94.7%, the specificity was 70%, the positive predictive value was 85.7%, and the negative predictive value was 87.5%. Further, direct false positive or false negative diagnostic conclusions based on the online protocol were absent when the possible conclusion of "I cannot decide" was included. The items B9 Showing, B10 Spontaneous Initiation of Joint Attention, B1 Unusual Eye Contact, B3 Facial Expressions Directed to Others, and C2 Imagination/Creativity were shown to be well observable in BOSA when related to ADOS-2 scoring. The results indicate that the protocol consisting of BOSA and ADI-R administered by an experienced examiner is a promising combination of tools for remote autism assessment.

Heterogeneous ensemble classifier in computer systems of medical diagnostics

The work is devoted to the solution of an actual scientific and technical problem tasks development of a support system for making diagnostic decisions in medicine based on the developed model of a heterogeneous ensemble classifier, which as basic models implements two approaches to development of diagnostic conclusions: probabilistic , based on the analysis of the training sample and expert information on the structure of symptom complexes of each disease. As a probabilistic component, the choice of the method of comparison with the standard, in which the diagnosed conditions of patients are represented by their standards in the space of signs, is justified. The geometric center of the class grouping in the space of diagnostic features is chosen as the benchmark for each class. Expert knowledge about the structure of the symptom complex is formalized by expressing the symptom complex of the disease in the form of numerical intervals of linguistic changes "below the norm", "norm", "above the norm". Various options for taking into account the opinion of experts about the structure of symptom complexes in the ensemble classifier are considered. Test verification of the developed classifier was conducted on real medical data, which confirmed its effectiveness.

Medication reconciliation enhances the accuracy of gastric emptying scintigraphy

BackgroundGastroparesis (GP) is a prevalent sensorimotor disorder characterized by delayed gastric emptying without mechanical obstruction, posing significant diagnostic challenges. Gastric emptying scintigraphy (GES) is the gold standard for diagnosing GP. However, its accuracy can be compromised by many medications that affect gastric motility. This study evaluates the impact of medication reconciliation on the diagnostic accuracy of GES.ResultsA significant proportion of patients (75%) were on medications known to affect gastric motility. Recommendations for medication adjustments were communicated, with 30% non-adherence. Adjustments in GES interpretations were necessary for 20% of patients following comprehensive medication reviews. The involvement of radiopharmacists facilitated accurate diagnostic conclusions, underscoring the critical role of medication reconciliation in GES accuracy.ConclusionMedication reconciliation enhanced the accuracy of GES in diagnosing gastroparesis, emphasizing the need to integrate clinical pharmacy practices into nuclear medicine. This interdisciplinary approach not only improves diagnostic accuracy but also enhances patient safety, advocating for the adoption of such practices in the management of gastroparesis.

Implementation of Extreme Learning Machine Method with Particle Swarm Optimization to Classify of Chronic Kidney Disease

Kidney Disease (CKD) appears as a pathological condition due to infection of the kidneys and blockages due to the formation of kidney stones. In the Indonesian context, kidney disease is the second most common disease after heart disease based on BPJS Health data. Notably, in this scenario, medical practitioners and individuals with specialized knowledge in the field are still faced with challenges in effectively classifying CKD cases, thereby making them vulnerable to erroneous diagnostic conclusions. The main objective underlying this particular research effort revolves around increasing the level of accuracy that characterizes the CKD classification process by orchestrating the incorporation of Particle Swarm Optimization (PSO) techniques into the operational framework of Extreme Learning Machines (ELM) with the aim of ensuring optimal results. Configuration of input weights and critical biases to achieve superior diagnostic results. The results obtained from the investigation process include many numerical parameters including but not limited to determining the ideal number of hidden nodes set at 11, population size 80, identification of the most preferred number of iterations denoted by the Best value of 20, aggregate inertia weight assessed at 0.5, along with the constants 1 (c1) and 2 (c2) each registering a value of 1, culminating in the achievement of an accuracy metric pegged at an impressive level of 98.50%. Consequently, the implications obtained from this empirical investigation strengthen the assertion that the use of PSO optimization strategies within the operational framework of ELM has the potential to yield major advances in the classification evaluation domain related to CKD diagnosis.

Diagnosing new-onset asthma in a paediatric clinical trial setting in school-age children.

Asthma is a common chronic disease in children. It is a dynamic condition-symptoms change over time, and the outcome of diagnostic tests can vary. Consequently, evaluating the onset of asthma at a single point in time, perhaps when patients are asymptomatic with limited impairment of the lung function, may result in false diagnostic conclusions. The absence of consistent gold-standard diagnostic criteria in children challenges the ability of any study to ascertain an effect of treatment on asthma prevention. A comprehensive review of the diagnostic criteria used for new-onset asthma in school-age children was conducted based on existing recommendations from published clinical guidance, alongside evidence from paediatric asthma prevention trials. Findings from the review were used to propose suggestions for diagnosing new-onset asthma in future asthma prevention trials. Despite an overall lack of consensus in the published clinical guidance, there are similarities between the various recommendations for diagnosing asthma in children, which typically involve assessing the variable symptoms and supplementing the medical history with objective measures of lung function. For future paediatric asthma prevention trials, we suggest that paediatric clinical trials should use a new-onset asthma definition that incorporates the concepts of "possible", "probable" and "confirmed" asthma. "Possible" asthma would capture self-reported features of chronic symptoms and symptom relief with β2-agonist bronchodilator (suggesting reversibility). "Probable" asthma would include symptom chronicity, self-reported symptom relief with β2-agonist bronchodilator, and objective features of asthma (reversibility or bronchial hyper-responsiveness). A "confirmed" diagnosis would be made only if there is a positive response to controller therapy. These suggestions aim to improve the diagnosis of new-onset childhood asthma in clinical trials, which will be useful in the design and conduct of future paediatric asthma prevention trials.

KGroot: A knowledge graph-enhanced method for root cause analysis

Fault localization in online microservices is a challenging task due to the vast amount of monitoring data, diversity of types and events, and complex interdependencies among services and components. Fault events in services are propagative and can trigger a cascade of faults in a short period of time. In the industry, fault localization is typically conducted manually by experienced personnel. This reliance on experience is unreliable and lacks automation. Different modules present information barriers during manual localization, making it difficult to quickly align during urgent faults. This inefficiency lags stability assurance to minimize fault detection and repair time. Although actionable methods aimed to automate the process, the accuracy and efficiency are less than satisfactory. The precision of fault localization results is of paramount importance as it underpins engineers’ trust in the diagnostic conclusions, which are derived from multiple perspectives and offer comprehensive insights. Therefore, a more reliable method is required to automatically identify the associative relationships among fault events and propagation paths. To achieve this, a knowledge graph-enhanced root cause analysis (KGroot) method is designed for efficient and effective diagnosis of recurring failures in complex microservices environments. As the first event-driven knowledge graph method, KGroot uses event knowledge and the correlation between events to perform root cause reasoning for Root Cause Analysis (RCA). A Fault Event Knowledge Graph (FEKG) is built based on historical data, an online graph is constructed in real-time when a failure event occurs, and the similarity between each event knowledge graph and online graph is compared using GCNs to pinpoint the fault type through a ranking strategy. Comprehensive experiments demonstrate that KGroot can locate the root cause with an accuracy of 93.5% top 3 potential causes in second-level. This performance matches the level of real-time fault diagnosis in the industrial environment and significantly surpasses state-of-the-art baselines in RCA in terms of effectiveness and efficiency. (KGroot is available at https://github.com/daixixiwang/KGroot).

Prediction of metastases in confusing mediastinal lymph nodes based on flourine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging using machine learning.

For patient management and prognosis, accurate assessment of mediastinal lymph node (LN) status is essential. This study aimed to use machine learning approaches to assess the status of confusing LNs in the mediastinum using positron emission tomography/computed tomography (PET/CT) images; the results were then compared with the diagnostic conclusions of nuclear medicine physicians. A total of 509 confusing mediastinal LNs that had undergone pathological assessment or follow-up from 320 patients from three centres were retrospectively included in the study. LNs from centres I and II were randomised into a training cohort (N=324) and an internal validation cohort (N=81), while those from centre III patients formed an external validation cohort (N=104). Various parameters measured from PET and CT images and extracted radiomics and deep learning features were used to construct PET/CT-parameter, radiomics, and deep learning models, respectively. Model performance was compared with the diagnostic results of nuclear medicine physicians using the area under the curve (AUC), sensitivity, specificity, and decision curve analysis (DCA). The coupled model of gradient boosting decision tree-logistic regression (GBDT-LR) incorporating radiomic features showed AUCs of 92.2% [95% confidence interval (CI), 0.890-0.953], 84.6% (95% CI, 0.761-0.930) and 84.6% (95% CI, 0.770-0.922) across the three cohorts. It significantly outperformed the deep learning model, the parametric PET/CT model and the physician's diagnosis. DCA demonstrated the clinical usefulness of the GBDT-LR model. The presented GBDT-LR model performed well in evaluating confusing mediastinal LNs in both internal and external validation sets. It not only crossed radiometric features but also avoided overfitting.

Dual process models of clinical reasoning: The central role of knowledge in diagnostic expertise.

Research on diagnostic reasoning has been conducted for fifty years or more. There is growing consensus that there are two distinct processes involved in human diagnostic reasoning: System 1, a rapid retrieval of possible diagnostic hypotheses, largely automatic and based to a large part on experiential knowledge, and System 2, a slower, analytical, conscious application of formal knowledge to arrive at a diagnostic conclusion. However, within this broad framework, controversy and disagreement abound. In particular, many authors have suggested that the root cause of diagnostic errors is cognitive biases originating in System 1 and propose that educating learners about the types of cognitive biases and their impact on diagnosis would have a major influence on error reduction. In the present paper, we take issue with these claims. We reviewed the literature to examine the extent to which this theoretical model is supported by the evidence. We show that evidence derived from fundamental research in human cognition and studies in clinical medicine challenges the basic assumptions of this theory-that errors arise in System 1 processing as a consequence of cognitive biases, and are corrected by slow, deliberative analytical processing. We claim that, to the contrary, errors derive from both System 1 and System 2 reasoning, that they arise from lack of access to the appropriate knowledge, not from errors of processing, and that the two processes are not essential to the process of diagnostic reasoning. The two processing modes are better understood as a consequence of the nature of the knowledge retrieved, not as independent processes.

Combined Biologic and Surgical Interventions for Hidradenitis Suppurativa: A Systematic Review

Introduction. Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease characterized by recurrent painful or suppurative lesions due to follicular occlusion. Biologics and other treatment modalities such as surgical excision are commonly used in the treatment of severe HS. However, despite the frequent use of biologics and surgical interventions in the treatment of patients with HS, an assessment of their combined effects is lacking. This systematic review aims to qualitatively analyze the efficacy of combined biologic and surgical treatment for HS. Methods. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were followed. The databases PubMed (MEDLINE), Embase, Cochrane (CENTRAL), ClinicalTrials.gov, MedRxiv.org, and the International Clinical Trial Registry were searched from inception until May 1, 2023. Results. A total of 1,145 studies were screened, with eight studies included for data extraction. Patients receiving combined biologic and surgical treatment showed greater improvement in the severity measurements of HS, including HS Impact Assessment, HS Physician Global Assessment, HS Sartorius Score, International Hidradenitis Suppurative Severity Score, HS recurrence rate, and Dermatology Life Quality Index. However, three studies reported a prolongation of wound healing with combined biologic and surgical treatment. Conclusion. Our systematic review highlights the additive effects of using biologics and surgery together to treat HS compared to either treatment alone. However, when both treatment modalities are used simultaneously, the potential risk of prolonged wound healing must be considered. Due to the limited number and heterogeneity of the included studies, more clinical trials are needed to establish diagnostic conclusions.

Disentangling the Diagnoses of Psychotic Depression, Catatonia, Neurocognitive Disorders, and Delirium: A Multidisciplinary Approach

An ambiguous clinical presentation can create diagnostic uncertainty, leading to delays in effective interventions and increasing the likelihood of adverse clinical outcomes. Unfortunately, many psychiatric conditions share overlapping clinical symptoms. Psychotic depression, catatonia, neurocognitive disorders, and delirium are all examples of distinct clinical conditions with overlapping symptomatology, and all require decidedly different therapeutic interventions. While much literature is devoted to the diagnostic criteria and management of these clinical conditions, little is dedicated to the challenge of disentangling these diagnoses when faced with a complex clinical presentation.We present here a complicated psychiatric inpatient hospitalization of a 77-year-old male who exemplified this diagnostic challenge. He primarily presented with severely depressed mood and decreased functionality, remaining in bed for much of the day with little attention to basic hygiene and oral intake. While records from a recent outside hospitalization carried a historical diagnosis of catatonia, his moderate level of engagement with providers and somatic delusion of disseminated prostate cancer suggested an alternate diagnosis of psychotic depression. However, before a diagnostic conclusion could be reached, other clinical characteristics surfaced. Following a singular administration of olanzapine shortly after his initial assessment, he developed Lilliputian hallucinations, prominent parkinsonism, and significantly altered mentation.Neuroleptic sensitivity raised clinical suspicion for an underlying neurocognitive disorder such as dementia with Lewy bodies, and a consult was placed to the neuropsychology service. While dementia could not be fully ruled out, they did recommend additional workup to rule out herpes simplex encephalitis, autoimmune limbic encephalitis, and paraneoplastic syndromes. After his MRI imaging revealed moderate microvascular ischemic changes, a true multidisciplinary approach to care was taken, as neurology was consulted to also help narrow the differential. An EEG was unremarkable and a serum encephalitis panel was also unremarkable. Olanzapine was discontinued with significant improvement in parkinsonism and cognition; however, severe neurovegetative symptoms and somatic and nihilistic delusions persisted. While antipsychotics remained clinically indicated for a suspected depression with psychotic features, caution was taken due to established neuroleptic sensitivity, subtle catatonic features, and the related risks of neuroleptic malignant syndrome. Ultimately, low-dose quetiapine was initiated with some benefit; however, he continued to remain acutely depressed and withdrawn with prominent negativism. Trials with benzodiazepines for these catatonic features were also approached with caution to avoid potential risks of delirium.Over time, a multidisciplinary approach to psychopharmacologic interventions and medication trials led the team to a principal diagnosis of major depressive disorder with psychotic features and subtle catatonia. The abrupt changes observed early in the hospital course were determined to be related to olanzapine-induced anticholinergic delirium. With greater diagnostic certainty, the team was able to counsel the patient and family about the importance of pursuing electroconvulsive therapy (ECT). The patient's cardiovascular risk factors and known microvascular changes were considered in the determination of the appropriateness for ECT. After starting ECT, he was immediately noted to have objective improvement, and following his fifth treatment, he was deemed appropriate for discharge after exhibiting significant improvement in depression and functionality and reporting near-resolution of somatic delusions. He was able to complete ECT on an outpatient basis and follow-up visits note durable improvements in mood, functionality, and quality of life, continuing to enjoy an active lifestyle of socializing with family and playing tennis. We use this case to further explore the clinical challenges that stem from the symptomatic similarity between psychotic depression, catatonia, neurocognitive disorders, and delirium and discuss optimal therapeutic approaches when the presentation appears complex and equivocal.

Fully automated diagnosis of thyroid nodule ultrasound using brain-inspired inference

The interpretability of artificial intelligence (AI) based medical diagnostic systems is crucial to make the diagnosis adequately convincible. Deep learning has been extensively investigated and utilized in the area of medical assistance diagnosis in recent decades due to its outstanding performance and objective prediction. However, a huge semantic chasm dividing clinicians and unexplainable deep models emerges. Here we design a brain-inspired inference framework from medical images to explainable features, then to the final diagnostic conclusions. The fast thinking module is responsible for recognizing medical features in ultrasound (US) images, and the slow-thinking module builds a model for inferring from medical features to diagnostic results by constructing a knowledge graph of medical features with tensor decomposition. The whole model infers through intuition and thinking like a human being, and gives the recognized medical image features while inferring the diagnosis, which greatly improves the interpretability of the model. We conducted studies on thyroid cancer diagnoses using US images. The American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) characteristics are employed as medical features describing thyroid nodules. Our brain-inspired medical inference framework outperforms commonly used deep learning algorithms, with an AUC score of 0.963 (95% confidential interval (CI)=0.923–1.000) for thyroid US image diagnosis. Results indicate that our framework improves diagnostic objectivity and interpretability while providing performance that is better than deep models. Our proposed brain-inspired medical inference framework could improve the efficiency of diagnosis and our technique is performant, objective and interpretable.

Autism Spectrum Disorder Diagnostic Scale – Revised

The main aim of this study is to empirically prove the Diagnostic Accuracy Scale to diagnosis of people with autism spectrum disorders (ASD), the sample of participants has been extended, paying special attention to students over 14 years of age, adapting the activities and, therefore, the indices-criteria of the observation factors. A total of 94 students have participated in the study, whose coded data allow us to corroborate the data found in the initial scale, facilitating a precise and exhaustive diagnosis of the disorder by levels. The diagnostic conclusions were based on the gaussian curve found from the typical scores obtained from the direct scores obtained in the ten dimensions of the analysis, made up of three dimensional categories, whose percentage weighting is as follows: 1) the procedural category (60%), 2) the social dimensional category (20%), and 3) the behavioural dimensional category (20%), the overall sum of which allows the diagnostic conclusions of the disorder to be drawn, both at a general level and according to the specific level of ASD. The percentage levels are justified by the empirical studies of the study. Thus, the stepwise linear regression analysis carried out includes two dimensions corresponding to the procedural category as basic predictors of the disorder: I) semantic retrieval (Beta: .90, R2: .82), and II) relationship between retrieval (Beta: .57) and interconcepts or relationships (R2: .82). 57) and interconcepts or relationships between information (Beta: .35), being the overall constant (Beta: .92, R2: .84), ahead of the social or behavioural dimensions, which supports the importance of the procedural dimension, especially related to the development of nodes or relationships between information content in determining the specific criteria for the diagnosis of ASD.

Impact of diagnostic management team on patient time to diagnosis and percent of accurate and clinically actionable diagnoses.

Diagnostic management teams (DMT) are groups of experts with specialized knowledge to guide test selection and interpretation of results. They have been active in institutions over the past 20 years. To date, there are limited data on whether the presence of experts to advise healthcare providers on appropriate laboratory test selection and interpretation of complex test results positively impacts patient care. A retrospective study at a regional healthcare system with 257,000 patient encounters between 2011 and 2022 reviewing test interpretations provided by clinical laboratory experts on a diagnostic management team. Cases reviewed by the coagulation DMT were 6 times more likely to have an established, scientifically based diagnosis compared to those without a DMT. Patients who have a coagulation DMT review were twice as likely to receive a diagnosis vs. having no diagnosis. This study demonstrates that for several objective clinical outcomes, specifically diagnostic conclusions and length of stay, a DMT of coagulation experts assessing patients' test results has had a major impact on outcomes and delivery of care.

Accuracy of GPT-4 in histopathological image detection and classification of colorectal adenomas

AimsTo evaluate the accuracy of Chat Generative Pre-trained Transformer (ChatGPT) powered by GPT-4 in histopathological image detection and classification of colorectal adenomas using the diagnostic consensus provided by pathologists as...

ДОСЛІДЖЕННЯ ПРОБЛЕМИ ДЕФІЦИТУ КАДРІВ НА РИНКУ ПРАЦІ НА ПРИКЛАДІ МЕБЛЕВОЇ ГАЛУЗІ

Modern russian-Ukrainian war deepened in February 2022 drastically reshaped the labour market, causing emigration and internal people displacement and as a consequence the significant workforce shortage for all industries, especially in manufacturing. Solving this problem is crucial for resolving several major challenges, including defence procurements. The relevance of this topic lies in on-date quantitative and qualitative analysis of labour market deficiencies during martial law. The aim of this article is to evaluate and analyse the labour market imbalance and workforce shortages in production industries, with a focus on the furniture industry, and to seek solutions to these problems. Observation disclosed that official statistical data is overdue and does not fully show the volume of the shortages. This article presents findings from the authors’ own research, scientific inquiries, reviews, surveys, and labour market monitoring by business communities and donor organizations. A survey of furniture industry enterprise representatives identified the distribution of personnel needs in production specialties across Ukrainian regions. In-depth interviews with industry experts provided diagnostic insights into the current state and prospects of the industry. The article also examines the trend of labour shortages across Ukraine’s labour market from the early months of the invasion to the present, using data from online job search portals. The increasing workforce shortage is illustrated through the furniture industry, with diagnostic conclusions about the industry’s state and prospects. The article develops recommendations for advancing the furniture industry and training new personnel for vacant production positions. Three options to address the labour market shortage are proposed: utilizing artificial intelligence and radical business process automation, attracting labour migrants from poorer countries, and establishing training and retraining centres. The initial results of testing the latter option for furniture companies demonstrate the approach’s potential. The article underscores the importance of addressing the personnel shortage issue for businesses and the state budget both now and in the coming years. The proposed measures can help overcome the labour market crisis, contribute to economic stabilization, and provide the necessary resources for the country’s recovery.

Análise ultrassonográfica e histopatológica da vesícula biliar de cães

ABSTRACT In Veterinary Medicine, diseases restricted to the gallbladder (GB) are uncommon in dogs. The histopathological examination of GB is considered the gold standard. However, abdominal ultrasound examination is increasingly included in the veterinary clinical routine, as it allows changes in previously underdiagnosed GB to have a more accurate diagnosis. The aim of this study was to identify, quantify and correlate the different aspects of GB in dogs through ultrasound and histopathological examinations. The GB of 60 dog cadavers were evaluated, without predilection for sex, age, and breed. The results demonstrated that the ultrasound examination, when compared to the histopathological examination, presented 83% agreement in the analysis of general data for the presence or absence of GB changes. The level of agreement in the Kappa test was considerably high (K=0.63). There was a high agreement in the diagnosis of changes observed by ultrasound and histopathological examinations, when compared “affected” or “non affected”. However, when compared the findings of mucocele, cystic mucinous hyperplasia and cholecystitis, there is a low Kappa agreement. Thus, ultrasound examination is an excellent tool for identifying changes related to the GB. However, more studies must be carried out to deepen the understanding of the correlation between imaging and pathological findings to lead diagnostic conclusions.

Autism Perceptual – Behavioural Precision Scale

The Perceptual-Cognitive-Behavioural Diagnostic Precision Scale for Autism Spectrum Disorder allows to complement the analysis of the autism diagnosis through the measurement of variables the neuropsychological processing of human information to avoid high errors over ASD diagnosis currently existing, derived from unilateral analysis of the behaviour criteria component of the actual Scales. The empirical scoring of the Scale has been verified to N= 75, being 38 participants belonging to the TEA-1 level, 24 to TEA-2 and 13 to TEA-3, has allowed find a statistical reliability of Cronbach's Alpha average greater to .91 in the ten dimensions of the Scale: 1) comprehension, 2) significant, 3) categories, 4) intercategorical 5) relationships-neural-nodes, 6) semantic recovery, 7) social interaction, 8) social communication, 9) stereotyped behaviours, and 10) restrictive behaviours. These ten dimensions have been statistically grouped around three great categories to analysis: 1) perceptual-cognitive processing, 2) social interaction, and 3) behaviour. The conclusive statistical analyses indicate that perceptual-cognitive process category explains 88.52% of total accumulated explicative variance, social category: 10.19% and behaviour: 1.28%; which shows the importance of the perceptual-cognitive dimensional factor analysis, in order to conclude with the mean percentiles of the diagnostic conclusion regarding each ASD´ level, according to International Classification of the American Psychiatric Association DSM-5 (APA, 2023).