12 research outputs found

    Language production impairments in patients with a first episode of psychosis

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    A multi-element psychosocial intervention for early psychosis (GET UP PIANO TRIAL) conducted in a catchment area of 10 million inhabitants: study protocol for a pragmatic cluster randomized controlled trial

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    Multi-element interventions for first-episode psychosis (FEP) are promising, but have mostly been conducted in non-epidemiologically representative samples, thereby raising the risk of underestimating the complexities involved in treating FEP in 'real-world' services

    Anxiety and depressive disorders in an emergency department ward of a general hospital: a control study

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    Objective: In this study anxiety and depressive disorders were evaluated in patients admitted to an emergency department (ED) or to a medical department (MD). Methods: The General Health Questionnaire-30 (GHQ-30) was administered to screen all patients (n = 719) consecutively admitted to an ED (n = 556) and to MD (n = 163) in a 120 day period. All GHQ-30 positive (score>4) underwent the Mini International Neuropsychiatric Interview, a structured interview to diagnose mental disorders according to DSM-IV criteria. Results: Subjects positive to GHQ-30 were 264 (47%) in ED and 88 (54%) in MD. A mental disorder was diagnosed in 233 ED patients (42%) and in 77 MD patients (47%) (p = 0.70). The most frequent disorders were anxiety disorders in ED patients (18.1%) and depressive disorders in MD patients (21%) (p = 0.04). Conclusions: Anxious patients more frequently seek attention at ED, whereas patients with depressive disorders are more often observed in medical units. The improvement of quality of care, the waste of healthcare resources through unnecessary medical care, and the well known efficacy of appropriate treatments in patients with anxiety and depressive disorders make the diagnosis of these patients particularly important

    Language production impairments in patients with a first episode of psychosis

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    Language production has often been described as impaired in psychiatric diseases such as in psychosis. Nevertheless, little is known about the characteristics of linguistic difficulties and their relation with other cognitive domains in patients with a first episode of psychosis (FEP), either affective or non-affective. To deepen our comprehension of linguistic profile in FEP, 133 patients with FEP (95 non-affective, FEP-NA; 38 affective, FEP-A) and 133 healthy controls (HC) were assessed with a narrative discourse task. Speech samples were systematically analyzed with a well-established multilevel procedure investigating both micro- (lexicon, morphology, syntax) and macro-linguistic (discourse coherence, pragmatics) levels of linguistic processing. Executive functioning and IQ were also evaluated. Both linguistic and neuropsychological measures were secondarily implemented with a machine learning approach in order to explore their predictive accuracy in classifying participants as FEP or HC. Compared to HC, FEP patients showed language production difficulty at both micro- and macro-linguistic levels. As for the former, FEP produced shorter and simpler sentences and fewer words per minute, along with a reduced number of lexical fillers, compared to HC. At the macro-linguistic level, FEP performance was impaired in local coherence, which was paired with a higher percentage of utterances with semantic errors. Linguistic measures were not correlated with any neuropsychological variables. No significant differences emerged between FEP-NA and FEP-A (p≥0.02, after Bonferroni correction). Machine learning analysis showed an accuracy of group prediction of 76.36% using language features only, with semantic variables being the most impactful. Such a percentage was enhanced when paired with clinical and neuropsychological variables. Results confirm the presence of language production deficits already at the first episode of the illness, being such impairment not related to other cognitive domains. The high accuracy obtained by the linguistic set of features in classifying groups support the use of machine learning methods in neuroscience investigations
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