"Conversational model" from_date:2012

Spiritual conversation model for patients and loved ones in palliative care: a validation study. In palliative care, validated tools for professionals that facilitate day-to-day spiritual conversations with patients and loved ones are scarce. The objective of this study was to validate the Diamond spiritual conversation model across different palliative care settings as well as professional

A randomised trial of dialectical behaviour therapy and the conversational model for the treatment of borderline personality disorder with recent suicidal and/or non-suicidal self-injury: An effectiveness study in an Australian public mental health servi Borderline personality disorder is a complex mental disorder that is associated with a high degree of suffering for the individual. Dialectical behaviour therapy has been studied in the largest number of controlled trials for treatment of individuals with borderline personality disorder. The conversational model is a psychodynamic treatment also developed specifically for treatment of borderline personality disorder. We report on the outcomes of a randomised trial comparing dialectical behaviour therapy and conversational model for treatment

Target dose conversion modeling from pencil beam (PB) to Monte Carlo (MC) for lung SBRT A challenge preventing routine clinical implementation of Monte Carlo (MC)-based lung SBRT is the difficulty of reinterpreting historical outcome data calculated with inaccurate dose algorithms, because the target dose was found to decrease to varying degrees when recalculated with MC. The large variability was previously found to be affected by factors such as tumour size, location, and lung density, usually through sub-group comparisons. We hereby conducted a pilot study to systematically and quantitatively analyze these patient factors and explore accurate target dose conversion models, so that large-scale historical outcome data can be correlated with more accurate MC dose without recalculation. Twenty-one

of staffing models. In an ICU with 24/7 attending in-house coverage, every hour is covered by an attending physician. Conversely, models without 24/7 in-house presence would require significantly fewer FTE, but must account for patient management in the absence of a bedside intensivist. Lastly, time used as a surrogate does not reflect the intensity and desirability of the shift. Nights and weekends can

showed that EES factorial models based on confirmatory factor analysis (CFA) roundly had poor fit. Conversely, models based on exploratory structural equation modelling (ESEM)-which accounts for the fact that EES items cross-load across factors-had adequate fit to the data. Additionally, we found that both higher-order and bifactor-ESEM models that controlled for the uniqueness of negatively worded

Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI. Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients' well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user

, adjusting for confounders. Conversion models were developed using a random half of the participants, and the observed and predicted utility values in the other half were compared to evaluate the model performance. Among the participants, 55.2% scored 0, 20.9% scored 1-5 and 23.9% scored 6-56 on the OHIP-14, corresponding mean utility values of 0.93, 0.90 and 0.84, respectively. A one-point increase in the OHIP-14 score was associated with a lower utility value (coefficient: -0.0053; 95% confidence interval:health-related quality of life -0.0056, -0.0051). The estimated utility value attributable to OHIP-14 was -23.3 per 1000 individuals, greater than that for other prevalent chronic conditions, including hypertension and diabetes (-2.9 and -7.1 per 1000 individuals, respectively). The conversion model

) and in mass flux (mass concentration). Of urgent need is to establish efficient conversion models to correlate these two important paradigms. Here, we first established an abundant environmental microplastic dataset and then employed a deep neural residual network (ResNet50) to successfully separate microplastics into fiber, fragment, and pellet shapes with 92.67% accuracy. We also used the circularity

symptoms in a chemically-induced mouse model of PD. However, follow-up studies have questioned the validity of this astrocyte-to-DAN conversion model. Here, we devised an adenine base editing strategy to downregulate PTBP1 in astrocytes and neurons in a chemically-induced PD mouse model. While PTBP1 downregulation in astrocytes had no effect, PTBP1 downregulation in neurons of the striatum resulted

versus reference tumor volumes were comparable (mean ± SD) (29.1 ± 42.2-cc versus 27.1 ± 32.9-cc, p = 0.69, CCC = 0.93). Inter-reader variability was high (mean DSC 0.69 ± 0.16), especially for smaller and isodense tumors. Conversely, model's high performance was comparable between tumor stages, volumes and densities (p > 0.05). Model was resilient to different tumor locations, status of pancreatic

in clinical documentation. ChatGPT is an artificial intelligence conversational model that generates human-like responses to text-based prompts. In this study, we sought to investigate ChatGPT's ability to assist with writing a history of present illness based on standardized patient histories. A blinded, randomized controlled study was conducted to compare the use of typing, dictation, and ChatGPT as tools

in only two dimensions (2D). The best-existing 2D to 3D conversion models require calibration for each new set of particles, which is labor-intensive. Here we introduce a new model that does not require calibration and compare its performance with existing models, including calibration-based ones. For the evaluation, we developed a new method in which the volumes of environmentally relevant microplastic

of this conversation model. Therefore, the aim of this study was to identify barriers and enablers during the implementation of the SICP in hospital settings. The SICP was implemented at 20 units in two hospitals in Sweden. During the implementation process, seven individual interviews and two group interviews were conducted with seven facilitators (five physicians, one behavioral therapist, and one administrator

ELISA. A catalytic conversion model was used to assess the transmission intensity of P. vivax malaria based on the maximum likelihood of generating a community seroconversion rate. A total of 3064 valid blood samples were collected. Antibody levels were positively correlated with age. The seroconversion rate (SCR) values for each village were Luoping (0.0054), Jingqiao (0.0061), Longpen (0.0087), Eluo

rule to rule out bloodstream infection in the emergency department: retrospective multicentric observational cohort study  Emergency Medicine JournalAccuracy and feasibility of three-dimensional ultrasound testing in eye clinic and emergency department patients with vision complaints  The Journal of Emergency MedicineAgreement of pCO2 in venous to arterial blood gas conversion models

be converted from contrast-enhanced DLCT scans, independent from the used scan phase. • DLCT-BMD measurements from contrast-enhanced scans should be adjusted with iodine concentrations of portal vein and/or abdominal aorta, which significantly improves the goodness-of-fit of conversion models.

%, respectively. Model 1 showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 66%, 91%, 92%, 63%, 86%, respectively. Conversely, Model 2 demonstrated the following sensitivity, specificity, negative predictive value, positive predictive value and accuracy: 82%, 58%, 74%, 69%, 71%, respectively. Time of analysis was significantly lower using CNN as compared