Diagnosis Of Illness Research Articles

Prevalence of a dual diagnosis of mental illness and substance use disorder in Ontario, Canada: a retrospective cohort study using linked administrative data

ObjectivePopulation-based prevalence estimates of co-occurring mental illness and substance use disorder (herein dual diagnosis) are scarce and derived from a single source (e.g., survey data) which may lead to underestimation of prevalence. We linked administrative data to a representative mental health survey in Ontario, Canada to estimate dual diagnosis prevalence using a data triangulation method. Approach We retrospectively linked the 2002 and 2012 Canadian Community Health Survey on Mental Health (CCHS-MH) to emergency department, inpatient hospital, and outpatient physician records in Ontario, Canada. Mental illness and substance use disorder were ascertained though self-report, Composite International Diagnostic Interview screening, and linked administrative health records. We estimated 1-year, 5-year, and lifetime prevalence of dual diagnosis. ResultsOf the CCHS-MH survey participants, 14,790 (99.8%) were included in the study. The 1-year, 5-year, and lifetime prevalence of dual diagnosis was 2.3%, 4.8%, and 9.8%, respectively, which attenuated to 2.1%, 4.1%, and 8.4%, respectively, when tobacco use disorder was removed from substance use disorder ascertainment. ConclusionDual diagnosis is more common in the general population than previously estimated. Considering barriers to accessing care for both mental illness and substance use disorders among people with dual diagnosis annually, our 5-year prevalence estimate is likely most informative for health system planning. ImplicationsIn the context of increased health burden, barriers to care access, and challenges to effective treatment associated with dual diagnosis, our findings present a case for increased investment in integrated models of mental healthcare and addiction medicine.

Association Analysis of Demographic Factors and the Likelihood of Self-reporting Mental Illness at Student Run Clinics

Background: Patients in the student-run clinics operated by the Tulane University School of Medicine report higher rates of previous diagnoses of mental illness (45.3%) when compared to the national average (22.8%). The diverse group of patients who utilize these resources have various demographic factors that may influence the likelihood of reporting a mental illness. Our objective is to analyze data from Tulane University School of Medicine’s student-run clinics to identify associations of self-reported psychological diagnoses with demographic and psychosocial factors. Methods: Data collected from surveys filled out by students interviewing patients in student-run clinics (n=1494), chi-square analysis, and multivariate regression analysis were conducted to evaluate associations between self-reported psychiatric illness with demographic and psychosocial factors. Results: In the chi square analysis, patients who were age 18-39, reported using psychiatric medications, identified as White, were female, did not report being homeless, had a previous suicide attempt, had a history of trading drugs for sex or money, were a victim or witness of a violent or traumatic event, and had a history of physical, emotional, and sexual abuse were more likely to report a mental health condition. Patients aged 60-99, who were African American, male, reported homelessness were less likely to report a history of a mental health conditions. Persons who identified as male were the only category statistically associated with decreased odds of reporting a mental health diagnosis. In the regression analysis, patients aged 18-39, aged 40-59, who were female, white, who had a suicide attempt, and had a history of incarceration were likely to report a mental health diagnosis. Conclusions: Patients with specific demographic and psychosocial characteristics were more likely to report a mental health diagnosis.

Una diagnosi difficile da accettare

An 18-month-old girl presented with low weight. Her parents denied her sickle cell anaemia diagnosis, confirmed at birth and neglected in treatment. Social and legal intervention was needed to ensure disease management. The case emphasizes the importance of clear communication and parental cooperation, especially in case of the diagnosis of a chronic illness.

Customer Awareness of Medical Wearable Health Care Technology and Policy Management in India

Patient and healthcare providers can take preventive actions based on a review of vital signs obtained remotely with wearable medical devices thanks to Internet of Things-based wearable sensors and an integrated cloud platform. Provide essential home care that is as clean and well-manicured as the intensive care unit (ICU), complete with remote vital sign monitoring. In India, there is no governing agency for healthcare. Healthcare professionals are concerned about the dependability of medical devices in the absence of sufficient laws, which makes them prefer to employ conventional medical devices. Based on related dangers, medical gadgets are to be divided into four types. Converging, analysing, and applying massive data collected by wearable medical devices for use in illness diagnosis, mitigation, and preventive therapy is a challenge to the medical business. By exchanging real-time data, the use of wearable medical devices would not only aid to strengthen patient-physician relationships but also decrease the need for human interventions. To uncover consumer awareness difficulties in the adoption of these devices, this paper explores the possibilities of the Internet of Things in the medical wearable business.

Decision Support System Based on Deep Learning with Big Data for Pneumonia Prediction

A bacterial, viral, or fungal infection of the lungs causes pneumonia in humans. It is the primary cause of global decline. Around 27% of deaths worldwide are attributable to this illness. A World Health Organisation report states that this serious infectious disease claims the lives of almost 2.5 million people annually. Early detection of chest illness, including pneumonia, can reduce the likelihood that the condition will worsen by allowing for the proper course of treatment. Accurate CT scans and CXR (chest X-ray) reports are renowned for being exact methods of diagnosis and prediction. This approach is trustworthy and non-invasive, and it can be used as a screening tool when a disease diagnosis is necessary. Nevertheless, research has demonstrated that even highly trained radiologists regularly fail to correctly identify pneumonia disease in its early stages. The severity of the sickness and the death rate increase as a result of diagnostic delays. Machine learning has affected every aspect of human life in the twenty-first century. It has a significant effect on the medical field in terms of early diagnosis of serious illnesses. Therefore, an intelligent health care system based on artificial intelligence (AI) was presented in this work for the early and accurate diagnosis of pneumonia disease.

Long COVID Illness: Disparities in Understanding and Receipt of Care in Emergency Department Populations

BackgroundMost Long COVID studies rely on traditional surveillance methods that miss underserved populations who use emergency departments (EDs) as their primary health care source. ObjectivesIn medically-underserved ED populations, we sought to determine 1) whether there are gaps in awareness and self-declared understanding about Long COVID illness, and 2) the prevalence, impact, and receipt of care for Long COVID symptoms. Design, Setting and ParticipantsCross-sectional, convenience sample survey study of adult patients at 11 geographically representative US EDs from 12/2022-10/2023. MeasurementsAwareness and self-declared understanding about Long COVID illness; prevalence, impact, and receipt of care for Long COVID symptoms. ResultsOf 1618 eligible patients,1455 (89.9%) agreed to participate: 33.4% African American, 30.9% Latino/a, and 17.1% lacked primary care; 33.2% had persistent COVID symptoms lasting > 1-month, 20.3% had symptoms > 3-months; 49.8% with Long COVID symptoms missed work/school because of symptoms; 30.3% of all participants and 33.5% of participants who had Long COVID symptoms had prior awareness and self-declared understanding of Long COVID. Characteristics associated with poor understanding of Long COVID were: African American race (adjusted odds ratio [aOR] 3.68, 95% confidence interval [CI] 2.66-5.09) and Latino/a ethnicity (aOR 3.16, 95% CI 2.15-4.64). Participants lacking primary care were less likely to have received Long COVID care (24.6% vs 51.2%; difference 26.6%; 95% CI 13.7-36.9%). Conclusions/RelevanceDespite high prevalence and impact of Long COVID symptoms, most of this ED population had limited awareness and self-declared understanding of Long COVID and many had not received care. EDs should consider development of protocols for diagnosis, education, and treatment of Long COVID illness.

Framework towards critical event classification of bipolar disorder in internet of things ecosystem

<p><span lang="EN-US">Bipolar disorder is quite a challenging mental illness which encounters substantial degree of challenges in confirmed diagnosis irrespective of modernized increasing pace of development in medical science. With the evolving standards of automation in healthcare section integrated with advanced technology, it is imperative to anticipate a reliable on-line diagnosis of mental illness for a given scenario of internet of things (IoT). Review of existing methodologies showcases a wide gap between enormous research work towards identification of bipolar disorder and only few studies towards on-line diagnosis considering patients residing in smart city. Therefore, the proposed scheme introduces a novel computational framework of an underlying architecture of an IoT that not only facilities an effective and simplified transmission of multimodal data autonomously from the patient forwarded to clinical analytical unit but also perform a multitier classification using deep neural network. The study outcome exhibits proposed scheme to offer better data transmission with higher accuracy performance in contrast to existing prevalent schemes.</span></p>

Pathways of Care in the Hospital Visiting Patients Having Mental Health Problems in Central India

Background: There is a high community prevalence of mental health problems in India. Psychiatric patients often consult nonspecialist or unqualified health providers which results in significant treatment delays and poor mental health outcomes. Knowledge of existing treatment-seeking practices may be useful to design specific and effective awareness programs. Objectives: The objectives were to study the types of health-care providers contacted by patients suffering from different mental illnesses as well as assess the treatment delays and associated factors. Methodology: A cross-sectional observational descriptive study was done in a psychiatry clinic of a tertiary health care center and teaching hospital in Central India. The information was collected from consecutive newly visiting patients on two predefined weekdays through face-to-face interviews. A predesigned sociodemographic format and World Health Organization pathways interview schedule were used to assess pathways of care. Results: Data were collected from 157 participants. Majority of participants were males (58%). Mood disorders were the most common diagnostic category (28%), followed by substance-related disorders (23%) and anxiety disorders (21%). Eighty (51%) among the total participants consulted psychiatrist in the first visit. Allopathic practitioners were the second commonly consulted health provider category. The median delay in treatment was maximum in substance-related disorders (7 years). Conclusion: Significant delay in treatment was observed across all diagnostic categories. Training general practitioners in the diagnosis and treatment of mental illnesses and a structured health-care referral system to enhance appropriate psychiatry consultations may be desirable to improve mental health outcomes in India.

Personal independence payments among people who access mental health services: results from a novel data linkage.

Personal independence payment (PIP) is a benefit that covers additional daily living costs people may incur from a long-term health condition or disability. Little is known about PIP receipt and associated factors among people who access mental health services, and trends over time. Individual-level data linking healthcare records with administrative records on benefits receipt have been non-existent in the UK. To explore how PIP receipt varies over time, including PIP type, and its association with sociodemographic and diagnostic patient characteristics among people who access mental health services. A data-set was established by linking electronic mental health records from the South London and Maudsley NHS Foundation Trust with administrative records from the Department for Work and Pensions. Of 143 714 working-age patients, 37 120 (25.8%) had received PIP between 2013 and 2019, with PIP receipt steadily increasing over time. Two in three patients (63.2%) had received both the daily living and mobility component. PIP receipt increased with age. Those in more deprived areas were more likely to receive PIP. The likelihood of PIP receipt varied by ethnicity. Patients diagnosed with a severe mental illness had 1.48 odds (95% CI 1.42-1.53) of having received PIP, compared with those with a different psychiatric diagnosis. One in four people who accessed mental health services had received PIP, with higher levels seen among those most likely in need, as indicated by a severe mental illness diagnosis. Future research using this data-set could explore the average duration of PIP receipt in people who access mental health services, and re-assessment patterns by psychiatric diagnosis.

Mapping Surgical Intervention Trajectories in Seriously Ill Children Receiving Palliative Care

Background/purposeDespite the prevalence of surgical intervention in seriously ill children, data is scarce regarding interventions performed based on type of serious illness. We therefore sought to evaluate the surgical interventions performed from the time of serious illness diagnosis to the present in a cohort of children receiving palliative care, including identification of the surgical specialists involved in these procedures. MethodsWe conducted a retrospective cohort analysis of surgical interventions in 197 children enrolled in a multicenter prospective cohort study (Pediatric Palliative Care Research Network SHARE Study). All surgical interventions were abstracted via clinical record review. Results189 (of 197, 96%) patients (45% female) with an average of 5.3 complex, chronic conditions (CCC) underwent 3331 surgical interventions (median = 13) by 21 specialist teams (most commonly general surgeons). Those with hematologic malignancies underwent intervention most frequently, followed by children with respiratory, genetic/metabolic, and gastrointestinal/genitourinary (GI/GU) diagnoses. Children with cardiovascular disease, malignancies, and prematurity had the shortest time between diagnosis and first intervention and between diagnosis and pediatric palliative care (PPC) services. By contrast, those with genetic, neurologic, and respiratory diagnoses had significantly longer intervals between diagnosis and intervention. ConclusionsNearly all seriously ill children receiving PPC undergo surgical intervention, and many undergo tens of interventions by a variety of subspecialist teams. Surgical intervention differs by serious illness type, with children with more acutely life-limiting illnesses undergoing high-volume, high-risk interventions in the immediate post-diagnosis period. Those with chronic, life-limiting illnesses undergo a higher lifetime volume of interventions that are relatively lower risk and more evenly distributed over time. Level of EvidenceN/A.

Integration and validation of host transcript signatures, including a novel 3-transcript tuberculosis signature, to enable one-step multiclass diagnosis of childhood febrile disease

BackgroundWhole blood host transcript signatures show great potential for diagnosis of infectious and inflammatory illness, with most published signatures performing binary classification tasks. Barriers to clinical implementation include validation studies, and development of strategies that enable simultaneous, multiclass diagnosis of febrile illness based on gene expression.MethodsWe validated five distinct diagnostic signatures for paediatric infectious diseases in parallel using a single NanoString nCounter® experiment. We included a novel 3-transcript signature for childhood tuberculosis, and four published signatures which differentiate bacterial infection, viral infection, or Kawasaki disease from other febrile illnesses. Signature performance was assessed using receiver operating characteristic curve statistics. We also explored conceptual frameworks for multiclass diagnostic signatures, including additional transcripts found to be significantly differentially expressed in previous studies. Relaxed, regularised logistic regression models were used to derive two novel multiclass signatures: a mixed One-vs-All model (MOVA), running multiple binomial models in parallel, and a full-multiclass model. In-sample performance of these models was compared using radar-plots and confusion matrix statistics.ResultsSamples from 91 children were included in the study: 23 bacterial infections (DB), 20 viral infections (DV), 14 Kawasaki disease (KD), 18 tuberculosis disease (TB), and 16 healthy controls. The five signatures tested demonstrated cross-platform performance similar to their primary discovery-validation cohorts. The signatures could differentiate: KD from other diseases with area under ROC curve (AUC) of 0.897 [95% confidence interval: 0.822–0.972]; DB from DV with AUC of 0.825 [0.691–0.959] (signature-1) and 0.867 [0.753–0.982] (signature-2); TB from other diseases with AUC of 0.882 [0.787–0.977] (novel signature); TB from healthy children with AUC of 0.910 [0.808–1.000]. Application of signatures outside of their designed context reduced performance. In-sample error rates for the multiclass models were 13.3% for the MOVA model and 0.0% for the full-multiclass model. The MOVA model misclassified DB cases most frequently (18.7%) and TB cases least (2.7%).ConclusionsOur study demonstrates the feasibility of NanoString technology for cross-platform validation of multiple transcriptomic signatures in parallel. This external cohort validated performance of all five signatures, including a novel sparse TB signature. Two exploratory multi-class models showed high potential accuracy across four distinct diagnostic groups.

Spatial attention U-Net model with Harris hawks optimization for retinal blood vessel and optic disc segmentation in fundus images.

The state of the human eye's blood vessels is a crucial aspect in the diagnosis of ophthalmological illnesses. For many computer-aided diagnostic systems, precise retinal vessel segmentation is an essential job. However, it remains a difficult task due to the intricate vascular system of the eye. Although many different vascular segmentation techniques have already been presented, additional study is still required to address the problem of inadequate segmentation of thin and tiny vessels. In this work, we introduce the Spatial Attention U-Net (SAU-Net) modelwith harris hawks' optimization (HHO), a lightweight network that can be applied as a data augmentation technique to improve the efficiency of the existingannotated samples without the need of thousands of training instances for Retinal Blood Vessel and Optic Disc Segmentation. The SAU-Net-HHO implementation uses a spatially inferred attention map multiplied by the input feature map for adaptive feature enhancement. U-Net convolutional blocks have been replaced with structured dropout blocks in the proposed network to prevent overfitting. Data from both vascular extraction (DRIVE) and structured analysis of the retina (STARE) are used to evaluate SAU-Net-HHO performance. The results show that the proposed SAU-Net-HHO performs well on both datasets. Analysing the obtained results, an average of 98.5% accuracy and Specificity 96.7% was achieved for DRIVE dataset and 97.8% accuracy and specificity 94.5% for STARE dataset. The proposed method yields numerical results with average values that are on par with those of state-of-the-art methods. Visual inspection has revealed that the suggested method can segment thin and tiny vessels with greater accuracy than previous methods. It also demonstrates its potential for real-life clinical application.

Stroke and Guillain-Barre syndrome secondary to spotted fever –A case report

Rickettsial infection is an important differential diagnosis in prolonged febrile illness and in returned travellers.We report a 44-year-old male from Deraniyagala, Sri Lanka, with spotted fever Rickettsiosis presenting with stroke and Guillain-Barre syndrome. He presented with dysarthria, reduced sensorium, and acute renal failure on the background of a prolonged febrile illness over two weeks. Following admission there was rapid deterioration of consciousness due to renal failure requiring intubation and repeated haemodialysis. On brain imaging, there were bilateral basal ganglia and internal and external capsule infarctions. His serology was positive for spotted fever Rickettsial infection and there was rapid improvement following treatment with doxycycline. However, difficulty in weaning off from the ventilator due to poor respiratory effort and symmetrical flaccid quadriparesis with areflexia raised concerns of concomitant Guillain-Barre syndrome. Serial cerebrospinal fluid (CSF) studies showed the development of elevated CSF protein with cytoalbuminologic dissociation during his stay in the intensive care unit (ICU). A diagnosis of co-existing Guillain-Barre syndrome was supported by the neuro-physiological findings and marked motor improvement following therapeutic plasmapheresis. This unmasked the right-sided mild residual hemiparesis he suffered due to stroke.

Technology Addiction and Social Connectedness in Psychiatric Illness: A Multicenter Study.

Technology addiction is an increasingly important public health problem all over the world that negatively affects people's mental and physical health. In this study, we examined technology addiction and social connectedness levels of psychiatric patients who admitted to clinics in different geographical regions of Turkey. A total of 642 people with a diagnosis of psychiatric illness who applied to psychiatry clinics in İstanbul, Elaziğ, Malatya, Yozgat, Adıyaman, and Bingöl provinces were included. Sociodemographic data form, Technology Addiction Scale (TAS), and Social Connectedness Scale (SCS) were applied to all participants. The total score of the TAS in patients diagnosed with anxiety disorder and somatoform disorder was significantly higher than the other patient groups (p<0.001). Patients diagnosed with anxiety disorder showed a significant difference from other patient groups in terms of SCS score (p<0.001). Anxiety disorder was found to be the highest in TAS total score and sub-dimensions and the lowest in SCS score, while major depressive disorder was found to be the lowest in TAS total score and sub-dimensions and the highest in SCS score. The multiple linear regression analysis showed that the total score of the TAS was predicted by the SCS score (β=-1.857, p<0.001) and the SCS score was predicted by age (β=0.046, p=0.049) and the total score of the TAS (β=-0.316, p<0.001). As a result of this study, we can say that psychiatric patients have a moderate level of technology addiction, these people have high levels of social connectedness, and psychiatric patients with technology addiction have a high level of social belonging.

Architecture Optimization for Hybrid Deep Residual Networks in Liver Tumor Segmentation Using a GA

Nowadays, liver tumor segmentation has been widely applied for vital medical objectives such as illness diagnosis, treatment, and evaluation of liver function. In this work, we aim to improve the performance of hybrid convolution neural network (CNN) models for liver tumor segmentation. A new automatic design method is introduced to build a hybrid CNN model with an optimal architecture using a combination of the original U_Net and the Res-UNet. In the proposed design method, the Res-UNet model is divided into different blocks, and then many different candidate architectures for the hybrid CNN model can be created using gradually these blocks to replace their corresponding blocks of the original U-Net. The replacement is executed if the candidate architecture using a residual block gives better performance than the corresponding one that uses the original U-Net block. We use a genetic algorithm (GA) to get the optimal architecture among the candidate architectures that gives the best performance for the hybrid CNN model. During the optimization process, in addition to the optimal architecture, other crucial configurations such as the learning algorithm, learning rate, and batch size are also determined. We refer to the hybrid CNN model that has the best architecture as GA_Res_UNet. Comparison has been made with other CNN models that are commonly used in image segmentation of liver tumors. We use the 3D-IRCADb01 liver tumor dataset that contains 2800 magnetic resonance (MR) images of 20 patients. Each image has a liver tumor with its corresponding mask. All MR images are resized to 256 × 256 in DICOM format. The findings indicate that GA_Res_UNet outperformed the other compared models over the considered dataset. It achieves 98.5% of dice coefficient and 99.8% of accuracy. Moreover, two other liver tumor segmentation datasets (LiTS17 and CHAOS) are utilized in another comparison to show the superior performance of our proposed model. The reported results show that the proposed model outperformed the other compared state-of-the-art models.

Association between a selective 5-HT4 receptor agonist and incidence of major depressive disorder: emulated target trial.

The serotonin 4 receptor (5-HT4R) is a promising target for the treatment of depression. Highly selective 5-HT4R agonists, such as prucalopride, have antidepressant-like and procognitive effects in preclinical models, but their clinical effects are not yet established. To determine whether prucalopride (a 5-HT4R agonist and licensed treatment for constipation) is associated with reduced incidence of depression in individuals with no past history of mental illness, compared with anti-constipation agents with no effect on the central nervous system. Using anonymised routinely collected data from a large-scale USA electronic health records network, we conducted an emulated target trial comparing depression incidence over 1 year in individuals without prior diagnoses of major mental illness, who initiated treatment with prucalopride versus two alternative anti-constipation agents that act by different mechanisms (linaclotide and lubiprostone). Cohorts were matched for 121 covariates capturing sociodemographic factors, and historical and/or concurrent comorbidities and medications. The primary outcome was a first diagnosis of major depressive disorder (ICD-10 code F32) within 1 year of the index date. Robustness of the results to changes in model and population specification was tested. Secondary outcomes included a first diagnosis of six other neuropsychiatric disorders. Treatment with prucalopride was associated with significantly lower incidence of depression in the following year compared with linaclotide (hazard ratio 0.87, 95% CI 0.76-0.99; P = 0.038; n = 8572 in each matched cohort) and lubiprostone (hazard ratio 0.79, 95% CI 0.69-0.91; P < 0.001; n = 8281). Significantly lower risks of all mood disorders and psychosis were also observed. Results were similar across robustness analyses. These findings support preclinical data and suggest a role for 5-HT4R agonists as novel agents in the prevention of major depression. These findings should stimulate randomised controlled trials to confirm if these agents can serve as a novel class of antidepressant within a clinical setting.

Automated system utilizing non-invasive technique mammograms for breast cancer detection

In order to increase the likelihood of obtaining treatment and achieving a complete recovery, early illness identification and diagnosis are crucial. Artificial intelligence is helpful with this process by allowing us to rapidly start the necessary protocol for treatment in the early stages of disease development. Artificial intelligence is a major contributor to the improvement of medical treatment for patients. In order to prevent and foresee this problem on the individual, family, and generational levels, Monitoring the patient's therapy and recovery is crucial. This study's objective is to outline a non-invasive method for using mammograms to detect breast abnormalities, classify breast disorders, and identify cancerous or benign tumor tissue in the breast. We used classification models on a dataset that has been pre-processed so that the number of samples is balanced, unlike previous work on the same dataset. Identifying cancerous or benign breast tissue requires the use of supervised learning techniques and algorithms, such as random forest (RF) and decision tree (DT) classifiers, to examine up to thirty features, such as breast size, mass, diameter, circumference, and the nature of the tumor (solid or cystic). To ascertain if the tissue is malignant or benign, the examination's findings are employed. These features are mostly what determines how effectively anything may be categorized. The DT classifier was able to get a score of 95.32%, while the RF satisfied a far higher 98.83 percent.

Did the Palliative Care Outcomes Collaboration (PCOC) program lead to improved end-of-life care quality and reduced non-beneficial treatments?

Palliative Care Outcomes Collaboration (PCOC) is an internationally recognized program developed in Australia. Taiwan became the first country in Asia to implement PCOC in 2020. There is little research on the impact of PCOC in Asia. We sought to examine the effects of the Taiwan PCOC on palliative outcomes. The study analyzed the impact of PCOC with a retrospective cohort design. The hypotheses were that PCOC could improve end-of-life care quality and reduce non-beneficial treatments. The study enrolled patients with terminal illnesses from the inpatient palliative care units. General characteristics of participants were collected. Exclusion criteria were people without a diagnosis of terminal illness, aged 20 and younger, or with missing data. The study collected 1,121 patients, 555 in the PCOC comparison group and 566 in the intervention group. Most patients were with terminal cancer (88.58%). The rates of hospital deaths in the PCOC and non-PCOC groups were 68.73% vs. 74.95% (P = 0.021). A multivariable logistic regression model, adjusting for age, sex, Charlson comorbidity index, and terminal cancer status, was utilized. The PCOC intervention significantly decreased hospital deaths (OR = 0.26, 95%CI 0. 16-0.41, P < 0.001). PCOC in Taiwan significantly reduced hospital deaths among terminal patients, possibly due to effective symptom management and improved communication via the use of patient-reported outcomes. Further research is needed to support PCOC implementation in Asia and investigate collaboration programs' impact on end-of-life care quality and non-beneficial treatments.

ABCDE assessment for palliative care truth-telling dilemmas: a case study.

In Indonesia, the diagnosis of a serious illness is often mediated through the patient's family, reflecting the cultural importance of family involvement in the patient's care and collective decision-making. To use a case study to show the difficulty that healthcare professionals face telling the patient the truth about their condition in Indonesia. The Kagawa-Singer and Blackhall ABCDE framework was used during truth-telling dilemmas to assess patients' and families' attitudes (A), beliefs (B), contexts (C), decision-making styles (D) and environments (E). Studies have shown that family involvement in health-related communications can alleviate the stress associated with the disclosure of a serious illness. Palliative care nurses must acknowledge the importance of family in the patient's cultural context, by involving them in the disclosure of a diagnosis and disease trajectory by integrating every element of the ABCDE model in palliative care.

Imaging diagnostics of pulmonary ground-glass nodules: a narrative review with current status and future directions.

The incidence rate of lung cancer, which also has the highest mortality rates for both men and women worldwide, is increasing globally. Due to advancements in imaging technology and the growing inclination of individuals to undergo screening, the detection rate of ground-glass nodules (GGNs) has surged rapidly. Currently, artificial intelligence (AI) methods for data analysis and interpretation, image processing, illness diagnosis, and lesion prediction offer a novel perspective on the diagnosis of GGNs. This article aimed to examine how to detect malignant lesions as early as possible and improve clinical diagnostic and treatment decisions by identifying benign and malignant lesions using imaging data. It also aimed to describe the use of computed tomography (CT)-guided biopsies and highlight developments in AI techniques in this area. We used PubMed, Elsevier ScienceDirect, Springer Database, and Google Scholar to search for information relevant to the article's topic. We gathered, examined, and interpreted relevant imaging resources from the Second Affiliated Hospital of Nanchang University's Imaging Center. Additionally, we used Adobe Illustrator 2020 to process all the figures. We examined the common signs of GGNs, elucidated the relationship between these signs and the identification of benign and malignant lesions, and then described the application of AI in image segmentation, automatic classification, and the invasiveness prediction of GGNs over the last three years, including its limitations and outlook. We also discussed the necessity of conducting biopsies of persistent pure GGNs. A variety of imaging features can be combined to improve the diagnosis of benign and malignant GGNs. The use of CT-guided puncture biopsy to clarify the nature of lesions should be considered with caution. The development of new AI tools brings new possibilities and hope to improving the ability of imaging physicians to analyze GGN images and achieving accurate diagnosis.