UNASSIGNED: The current paradigm in mental health care focuses on clinical recovery and symptom remission. This model\'s efficacy is influenced by therapist trust in patient recovery potential and the depth of the therapeutic relationship. Schizophrenia is a chronic illness with severe symptoms where the possibility of recovery is a matter of debate. As artificial intelligence (AI) becomes integrated into the health care field, it is important to examine its ability to assess recovery potential in major psychiatric disorders such as schizophrenia.
UNASSIGNED: This study aimed to evaluate the ability of large language models (LLMs) in comparison to mental health professionals to assess the prognosis of schizophrenia with and without professional treatment and the long-term positive and negative outcomes.
UNASSIGNED: Vignettes were inputted into LLMs interfaces and assessed 10 times by 4 AI platforms: ChatGPT-3.5, ChatGPT-4, Google Bard, and Claude. A total of 80 evaluations were collected and benchmarked against existing norms to analyze what mental health professionals (general practitioners, psychiatrists, clinical psychologists, and mental health nurses) and the general public think about schizophrenia prognosis with and without professional treatment and the positive and negative long-term outcomes of schizophrenia interventions.
UNASSIGNED: For the prognosis of schizophrenia with professional treatment, ChatGPT-3.5 was notably pessimistic, whereas ChatGPT-4, Claude, and Bard aligned with professional views but differed from the general public. All LLMs believed untreated schizophrenia would remain static or worsen without professional treatment. For long-term outcomes, ChatGPT-4 and Claude predicted more negative outcomes than Bard and ChatGPT-3.5. For positive outcomes, ChatGPT-3.5 and Claude were more pessimistic than Bard and ChatGPT-4.
UNASSIGNED: The finding that 3 out of the 4 LLMs aligned closely with the predictions of mental health professionals when considering the \"with treatment\" condition is a demonstration of the potential of this technology in providing professional clinical prognosis. The pessimistic assessment of ChatGPT-3.5 is a disturbing finding since it may reduce the motivation of patients to start or persist with treatment for schizophrenia. Overall, although LLMs hold promise in augmenting health care, their application necessitates rigorous validation and a harmonious blend with human expertise.

Congenital disorders of glycosylation (CDG) are a growing group of inherited metabolic disorders where enzymatic defects in the formation or processing of glycolipids and/or glycoproteins lead to variety of different diseases. The deficiency of GDP-Man:GlcNAc2-PP-dolichol mannosyltransferase, encoded by the human ortholog of ALG1 from yeast, is known as ALG1-CDG (CDG-Ik). The phenotypical, molecular and biochemical analysis of a severely affected ALG1-CDG patient is the focus of this paper. The patient\'s main symptoms were feeding problems and diarrhea, profound hypoproteinemia with massive ascites, muscular hypertonia, seizures refractory to treatment, recurrent episodes of apnoea, cardiac and hepatic involvement and coagulation anomalies. Compound heterozygosity for the mutations c.1145T>C (M382T) and c.1312C>T (R438W) was detected in the patient\'s ALG1-coding sequence. In contrast to a previously reported speculation on R438W we confirmed both mutations as disease-causing in ALG1-CDG.