Research indicates that contingency management (CM) has potential to improve a number of outcomes (e.g. substance use, treatment attendance, quality of life) among individuals with substance use and cooccurring disorders. However, multiple factors must be considered on a case-by-case basis in order to promote optimal treatment effects. The present study describes an individualized CM protocol for a US Veteran with substance dependence and cooccurring severe mental illness. CM targeted attendance at outpatient appointments and appropriate use of hospital resources. Effects of CM were assessed by comparing the 3-month baseline and CM periods. The CM intervention marginally reduced unnecessary hospital admissions, resulting in cost savings to the medical center of over $5,000 in three months for this individual. However, CM did not affect outpatient attendance. Several complications arose, highlighting challenges in using CM in populations with substance use and cooccurring disorders. Practical suggestions are offered for maximizing the effects of CM.
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http://dx.doi.org/10.1155/2012/731638 | DOI Listing |
Background: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and validate an explainable machine learning model to evaluate overall health status in patients with comorbid CHD and depression.
View Article and Find Full Text PDFNucleic Acids Res
January 2025
Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, 0372, Norway.
Machine learning (ML) has shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML methods where experimental data is currently inaccessible or insufficient.
View Article and Find Full Text PDFFront Immunol
January 2025
Department of Geriatric Medicine, Affiliated Hospital of Qingdao University, Qingdao, China.
Objective: This study aims to delineate the clinical features underlying the concurrent disease of neuromyelitis optica spectrum disorder (NMOSD) and myasthenia gravis (MG), and to identify efficacious therapeutic strategies.
Background: NMOSD and MG are uncommon autoimmune diseases that infrequently co-exist. Despite previous reports, a consensus on treating NMOSD concurrent with MG is lacking.
Br J Psychiatry
January 2025
Department of Psychology, Nottingham Trent University, UK; and Institute of Human Sciences, University of Oxford, UK.
Background: Reliable and specific biomarkers that can distinguish autism spectrum disorders (ASDs) from commonly co-occurring attention-deficit/hyperactivity disorder (ADHD) are lacking, causing misses and delays in diagnosis, and reducing access to interventions and quality of life.
Aims: To examine whether an innovative, brief (1-min), videogame method called Computerised Assessment of Motor Imitation (CAMI), can identify ASD-specific imitation differences compared with neurotypical children and children with ADHD.
Method: This cross-sectional study used CAMI alongside standardised parent-report (Social Responsiveness Scale, Second Edition) and observational measures of autism (Autism Diagnostic Observation Schedule-Second Edition; ADOS-2), ADHD (Conners) and motor ability (Physical and Neurological Examination for Soft Signs).
J Autism Dev Disord
January 2025
Institutes for Behavior Resources, Inc, 2104 Maryland Ave., Baltimore, MD, 21218, USA.
We aimed to compare sleep problems in autistic and non-autistic adults with co-occurring depression and anxiety. The primary research question was whether autism status influences sleep quality, after accounting for the effects of depression and anxiety. We hypothesized that autistic adults would report higher levels of depression, anxiety, and sleep problems compared to non-autistic adults, after controlling for these covariates.
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