Schizophrenia Research最新文献

{"title":"Loneliness is associated with different structural brain changes in schizophrenia spectrum disorders and major depression","authors":"Stefan Fritze ,&nbsp;Geva A. Brandt ,&nbsp;Sebastian Volkmer ,&nbsp;Jonas Daub ,&nbsp;Dilsa Cemre Akkoc Altinok ,&nbsp;Katharina M. Kubera ,&nbsp;Oksana Berhe ,&nbsp;Yuchen Lin ,&nbsp;Heike Tost ,&nbsp;Andreas Meyer-Lindenberg ,&nbsp;Dusan Hirjak","doi":"10.1016/j.schres.2025.01.001","DOIUrl":"10.1016/j.schres.2025.01.001","url":null,"abstract":"<div><h3>Background</h3><div>Loneliness, distress from having fewer social contacts than desired, has been recognized as a significant public health crisis. Although a substantial body of research has established connections between loneliness and various forms of psychopathology, our understanding of the neural underpinnings of loneliness in schizophrenia spectrum disorders (SSD) and major depressive disorder (MDD) remains limited.</div></div><div><h3>Methods</h3><div>In this study, structural magnetic resonance imaging (sMRI) data were collected from 57 SSD and 45 MDD patients as well as 41 healthy controls (HC). Loneliness was measured with the German version of the University of California, Los Angeles Loneliness Scale (UCLA-LS). We used FreeSurfer v7.2 for automated parcellation of cortical regions.</div></div><div><h3>Results</h3><div>SSD patients showed reduced cortical volume and thickness in fronto-parietal and temporal regions when compared to HC (<em>p</em> &lt; 0.05, Benjamini-Hochberg (BH) corr.). In SSD, volume of the right superior temporal gyrus was associated with UCLA-LS total score (<em>p</em> = 0.030; BH corr.). MDD patients showed reduced cortical volume and thickness in fronto-parietal regions (<em>p</em> &lt; 0.05, BH corr.). In MDD, cortical thickness of the right superior parietal cortex was associated with UCLA-LS total score (<em>p</em> = 0.038; BH corr.).</div></div><div><h3>Conclusion</h3><div>Our study suggests a different neural signature of loneliness in patients with SSD and MDD, comprising temporal and parietal regions responsible for social and attentive processing. Identifying neurobiological mechanisms underlying loneliness is critical for understanding its role in severe mental illnesses and identifying potential therapeutic targets.</div></div>","PeriodicalId":21417,"journal":{"name":"Schizophrenia Research","volume":"276 ","pages":"Pages 31-39"},"PeriodicalIF":3.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143024634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ambient coarse particulate matter pollution and hospital admissions for schizophrenia","authors":"Yating Ma ,&nbsp;Lijun Bai ,&nbsp;Yunxing Jiang ,&nbsp;Jinxi Wang ,&nbsp;Chen Wei ,&nbsp;Yinxiang Li ,&nbsp;Yumei Tian ,&nbsp;Shaowei Wu","doi":"10.1016/j.schres.2025.01.004","DOIUrl":"10.1016/j.schres.2025.01.004","url":null,"abstract":"<div><h3>Objective</h3><div>To investigate the association between ambient coarse particulate matter (PM_2.5_–₁₀) pollution and risk of acute schizophrenia episodes.</div></div><div><h3>Methods</h3><div>A time-stratified case-crossover study with a two-stage analytical approach was conducted to investigate the association between ambient PM_2.5_–₁₀ pollution and schizophrenia admissions (an indicator for acute schizophrenia episodes) across 259 Chinese cities of prefecture-level or above during 2013–2017. A conditional logistic regression model was constructed to estimate city-specific changes in hospital admissions for schizophrenia associated with per interquartile range (IQR) increase in ambient PM_2.5_–₁₀, and the overall associations were obtained by pooling the city-specific associations using the random-effects model.</div></div><div><h3>Results</h3><div>A total of 817,296 schizophrenia admissions were included in the analysis. Per IQR increase (28.43 μg/m³) in PM_2.5_–₁₀ at lag01 was associated with an increase of 1.66 % (95 % CI: 0.68 %, 2.65 %) in schizophrenia admissions. Compared to concentrations &lt;30 μg/m³, PM_2.5_–₁₀ concentrations of 30–49 μg/m³ and ≥50 μg/m³ were associated with increases of 2.25 % (95 % CI: 0.73 %%, 3.79 %) and 4.03 % (95 % CI: 1.92 %, 6.18 %) in schizophrenia admissions, respectively. City-level urbanization has the potential to attenuate the association between ambient PM_2.5_–₁₀ and schizophrenia admissions (<em>P</em> = 0.0002).</div></div><div><h3>Conclusions</h3><div>Our study provides novel evidence for the acute adverse effects of ambient PM_2.5_–₁₀ on schizophrenia and calls for special attention on the control of high PM_2.5_–₁₀ pollution in disease prevention.</div></div>","PeriodicalId":21417,"journal":{"name":"Schizophrenia Research","volume":"276 ","pages":"Pages 79-87"},"PeriodicalIF":3.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143041291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social stress in schizophrenia: Unique contributions to social cognition and social functioning","authors":"Kathryn C. Kemp ,&nbsp;Ivy F. Tso ,&nbsp;Stephan F. Taylor ,&nbsp;Aubrey M. Moe","doi":"10.1016/j.schres.2025.01.015","DOIUrl":"10.1016/j.schres.2025.01.015","url":null,"abstract":"<div><div>Schizophrenia-spectrum disorders have been associated with heightened stress sensitivity, which can worsen prognosis, functioning, and quality of life. However, more research is needed to determine whether different types of stress impact specific functional domains. This study used the Psychological Stress Index (PSI)—a self-report instrument designed and validated to measure perceived stress in psychosis—to delineate the unique contribution of social versus non-social stress to social functioning and social cognition. Fifty-nine participants with schizophrenia/schizoaffective disorder and fifty non-clinical controls completed the PSI and a battery of social functioning and social cognition measures. Elevated social stress statistically predicted worse performance on an emotion recognition task and worse interviewer-rated social functioning, over-and-above non-social stress. Higher social stress also statistically predicted worse interviewer-rated social functioning over-and-above performance on emotion recognition and theory-of-mind tasks. These results provide promising evidence that examining social stress separately from non-social stress provides unique information about social difficulties in schizophrenia-spectrum psychopathology. Examining social stress and other specific forms of stress may improve understanding of stress sensitivity in this population and better inform treatments aimed at improving functioning.</div></div>","PeriodicalId":21417,"journal":{"name":"Schizophrenia Research","volume":"276 ","pages":"Pages 167-174"},"PeriodicalIF":3.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-production of a state-funded centralized psychosis and psychosis risk screening, assessment, and referral service","authors":"Sarah L. Kopelovich,&nbsp;Kelsey Straub,&nbsp;Akansha Vaswani-Bye,&nbsp;Rachel M. Brian,&nbsp;Maria Monroe-DeVita","doi":"10.1016/j.schres.2024.12.011","DOIUrl":"10.1016/j.schres.2024.12.011","url":null,"abstract":"<div><div>Learning Health Systems (LHSs) strive to continuously integrate innovations and evidence-based practices in healthcare settings, thereby enhancing programmatic and patient outcomes. Duration of untreated psychosis (DUP) is a variable worthy of empirical attention, as the construct has been identified as a leading predictor of psychotic spectrum disorder prognosis and, despite the proliferation of early intervention for psychosis (EIP) teams across the U.S., remains longer than the recommended maximum established by the World Health Organization. Pathways to care are causally implicated as a DUP reduction rate-limiting factor. This paper illustrates a balanced care model, wherein resource-intensive community and clinical services are centralized to support a more efficient, standardized, and direct pathway to EIP care; identification of psychosis and psychotic risk states is made by highly-trained diagnosticians; and measurement-based care across the Learning Health System (LHS) is supported by a central assessment team. The Central Assessment of Psychosis Service (CAPS) streamlines core front-end EIP functions across the LHS, thereby alleviating the burden on EIP teams while enhancing access, equity, efficiency, and quality of the initial psychodiagnostic assessment. CAPS represents an innovative application of the balanced care model that preserves the core functions of the EIP team while task sharing or task shifting resource-intensive activities to an academic medical center partner. We review the five core functions of a centralized referral, screening, and assessment service. Given the potential for centralization to reduce DUP and enhance equity and access across the LHS, this paper will include concrete recommendations for policymakers considering centralizing core functions.</div></div>","PeriodicalId":21417,"journal":{"name":"Schizophrenia Research","volume":"275 ","pages":"Pages 196-207"},"PeriodicalIF":3.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142932651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does metacognitive training for psychosis (MCT) improve neurocognitive performance? A systematic review and meta-analysis","authors":"Clayton Jeffrey ,&nbsp;Danielle Penney ,&nbsp;Geneviève Sauvé ,&nbsp;Daniel Mendelson ,&nbsp;Élisabeth Thibaudeau ,&nbsp;Steffen Moritz ,&nbsp;Adèle Hotte-Meunier ,&nbsp;Martin Lepage","doi":"10.1016/j.schres.2024.12.004","DOIUrl":"10.1016/j.schres.2024.12.004","url":null,"abstract":"<div><h3>Background</h3><div>Metacognitive training for psychosis (MCT) offers benefits for addressing hallmark deficits/symptoms in schizophrenia spectrum disorders including reductions in cognitive biases and positive/negative symptoms as well as improvements in social cognition and functioning. However, differing results exist regarding the relationship between MCT and neurocognition. A comprehensive understanding of the nature of this relationship would significantly contribute to the existing literature and our understanding of the potential added value of MCT as a cognitive intervention for psychosis.</div></div><div><h3>Methods</h3><div>Across eleven electronic databases, 1312 sources were identified, and 14 studies examining MCT and neurocognition in psychosis were included in this review. Measures of estimated effect sizes were calculated with Hedge's <em>g</em>, moderator analyses used Cochrane's <em>Q</em> statistic and significance tests to measure group differences according to control conditions.</div></div><div><h3>Results</h3><div>Twelve studies, 11 randomized controlled trials (RCTs) and 1 non-RCT, were included in the main meta-analyses, consisting of 673 participants (n_MCT = 345, n_control = 328). When comparing MCT against control interventions, non-significant differences in estimated effect sizes were observed across all neurocognitive domains when evaluating pre–post changes (<em>g</em> ≤ 0.1, <em>p</em> &gt; .05). Two additional studies corroborated these results in a narrative review.</div></div><div><h3>Conclusion</h3><div>These findings suggest that when compared against control conditions, MCT does not pose a statistically meaningful benefit to neurocognitive performance. General practice/learning effects are likely the main contributor that explains improvement in neurocognitive performance, and not a difference of intervention allocation when considering MCT against the included control comparators. These findings help establish the specificity of the effects of MCT.</div></div>","PeriodicalId":21417,"journal":{"name":"Schizophrenia Research","volume":"275 ","pages":"Pages 79-86"},"PeriodicalIF":3.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142829845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response to clozapine and its predictors in treatment-resistant schizophrenia spectrum disorders: A retrospective chart review","authors":"Rajkumar Sanahan,&nbsp;Vanteemar S. Sreeraj,&nbsp;Satish Suhas,&nbsp;Vijay Kumar,&nbsp;Jagadisha Thirthalli,&nbsp;Ganesan Venkatasubramanian","doi":"10.1016/j.schres.2024.12.015","DOIUrl":"10.1016/j.schres.2024.12.015","url":null,"abstract":"<div><div>The response rate to clozapine in patients with treatment-resistant schizophrenia spectrum disorders (TRSS) is around 40 %. But, in general, a better prognosis is noted for schizophrenia in developing countries, including India. Given the scarcity of related literature from India, this study aimed to evaluate the response rates to clozapine in TRSS and explore predictors of response. Sociodemographic and clinical information from randomly selected 250 patients on clozapine for TRSS was collected through a retrospective chart review. Clozapine response was determined using the Clinical Global Impression-Schizophrenia scale at 6, 12, 24 weeks, and one year of initiating clozapine. Elastic net logistic regression analysis was performed to identify predictors of clozapine response. A total of 54 % responded to clozapine, with much or very much improvement in positive and overall symptoms of schizophrenia by the end of 12 weeks of clozapine initiation. Among all the responders at 12 weeks, 94 % continued to maintain response at one-year follow-up, and among non-responders, 34.2 % showed clinical improvement by 1-year follow-up. Lower symptom severity at baseline, good response to clozapine at six weeks, history of more suicidal attempts, and few other clinical symptoms like delusions and sociodemographic factors predicted a response to clozapine. A higher response rate (54 %) to clozapine is noted in 3rd month of clozapine, contrasting with the existing literature. Persistence of treatment could elicit further response over a year in early non-responders. Our study findings revealed that the demographic profile and clinical determinants may have an effect on clozapine response.</div></div>","PeriodicalId":21417,"journal":{"name":"Schizophrenia Research","volume":"275 ","pages":"Pages 179-188"},"PeriodicalIF":3.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142910441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Justice delayed? Wait times and behavioral emergencies in involuntary psychiatric treatment before and after COVID-19: A 3-year retrospective study","authors":"Reuben Heyman-Kantor ,&nbsp;Minnie Horvath Laury ,&nbsp;Esther Schoenfeld ,&nbsp;Cara Angelotta ,&nbsp;Ann Kan ,&nbsp;Kwang-Youn A. Kim ,&nbsp;Richard G. Cockerill","doi":"10.1016/j.schres.2024.11.012","DOIUrl":"10.1016/j.schres.2024.11.012","url":null,"abstract":"","PeriodicalId":21417,"journal":{"name":"Schizophrenia Research","volume":"275 ","pages":"Pages 217-219"},"PeriodicalIF":3.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142954243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Olfactory dysfunction in schizophrenia and other psychotic disorders: A comprehensive and updated meta-analysis","authors":"Letizia Zurlo ,&nbsp;Elisa Dal Bò ,&nbsp;Claudio Gentili ,&nbsp;Cinzia Cecchetto","doi":"10.1016/j.schres.2024.12.001","DOIUrl":"10.1016/j.schres.2024.12.001","url":null,"abstract":"<div><div>Olfaction plays a key role in our daily life, influencing food enjoyment, threat detection, mood and social relationships. Numerous studies have provided evidence of abnormal olfactory function in schizophrenia and other psychotic disorders. This pre-registered meta-analysis was conducted to (a) provide an updated overview of olfactory function in schizophrenia-spectrum disorders, and (b) examine the modulatory effects of demographic and clinical variables on distinct olfactory abilities. We complied with the PRISMA guidelines, searching throughout PubMed, MEDLINE, and PsycInfo, until the 12th of August 2023. A total of 73 publications were included, comprising data from 3282 patients and 3321 healthy controls. Results revealed that (a) patients performed significantly worse in higher-order olfactory tests (identification and discrimination) compared to healthy controls, while no differences were observed in odor sensitivity; (b) patients' performance in odor identification was moderated by education, as well as disease duration and negative symptoms. Our findings support the presence of olfactory impairments in schizophrenia-spectrum disorders, leading to significantly poorer performance in both odor identification and discrimination, but not sensitivity, when compared to healthy controls.</div></div>","PeriodicalId":21417,"journal":{"name":"Schizophrenia Research","volume":"275 ","pages":"Pages 62-75"},"PeriodicalIF":3.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics","authors":"Eun Young Kim ,&nbsp;Jayoun Kim ,&nbsp;Jae Hoon Jeong ,&nbsp;Jinhyeok Jang ,&nbsp;Nuree Kang ,&nbsp;Jieun Seo ,&nbsp;Young Eun Park ,&nbsp;Jiae Park ,&nbsp;Hyunsu Jeong ,&nbsp;Yong Min Ahn ,&nbsp;Yong Sik Kim ,&nbsp;Donghwan Lee ,&nbsp;Se Hyun Kim","doi":"10.1016/j.schres.2024.12.018","DOIUrl":"10.1016/j.schres.2024.12.018","url":null,"abstract":"<div><div>Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique, we aimed to formulate a model predicting antipsychotic treatment outcomes. Data were obtained from 299 patients with schizophrenia from three multicenter, open-label, non-comparative clinical trials. For prediction of treatment response at weeks 4, 8, and 24, psychopathology (both objective and subjective symptoms), sociodemographic and clinical factors, functional outcomes, attitude toward medication, and metabolic characteristics were evaluated. Various ML techniques were applied. The highest area under the curve (AUC) at weeks 4, 8 and 24 was 0.711, 0.664 and 0.678 with extreme gradient boosting, respectively. Notably, our findings indicate that BMI and attitude toward medication play a pivotal role in predicting treatment responses at all-time points. Other salient features for weeks 4 and 8 included psychosocial functioning, negative symptoms, subjective symptoms like psychoticism and hostility, and the level of prolactin. For week 24, positive symptoms, depression, education level and duration of illness were also important. This study introduced a precise clinical model for predicting schizophrenia treatment outcomes using multiple readily accessible predictors. The findings underscore the significance of metabolic parameters and subjective traits.</div></div>","PeriodicalId":21417,"journal":{"name":"Schizophrenia Research","volume":"275 ","pages":"Pages 146-155"},"PeriodicalIF":3.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142897084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-based medication adherence prediction in patients with schizophrenia and attenuated psychotic disorders","authors":"Zheng Zhu ,&nbsp;Dooti Roy ,&nbsp;Shaolei Feng ,&nbsp;Brian Vogler","doi":"10.1016/j.schres.2024.11.006","DOIUrl":"10.1016/j.schres.2024.11.006","url":null,"abstract":"<div><h3>Objective</h3><div>The capacity of machine-learning algorithms to predict medication adherence was assessed using data from AiCure, a computer vision-assisted smartphone application, which records the medication ingestion event.</div></div><div><h3>Methods</h3><div>Patients treated with BI 409306 were recruited from two Phase II randomized, placebo-controlled trials in schizophrenia (<span><span>NCT03351244</span><svg><path></path></svg></span>) and attenuated psychotic disorders (<span><span>NCT03230097</span><svg><path></path></svg></span>). A machine-learning model was optimized to predict overall trial adherence using AiCure data collected over three monitoring periods (7/10/14 days), adherence cut-offs (0.6/0.7/0.8) and timepoints (Start/Mid/End). Area under the curve (AUC), false negative rate, and false omission rate averaged across 10 model cross-validations were analyzed. In <span><span>NCT03351244</span><svg><path></path></svg></span>, post hoc analyses compared time to first relapse in patients observed as adherent versus those predicted adherent by the model.</div></div><div><h3>Results</h3><div>Of 235 patients, 60.4 % demonstrated ≥80 % adherence. At an adherence cut-off of 0.8, the 14-day model performed best (AUC: 0.81 versus 0.79 [10-day], 0.77 [7-day]). Within the 14-day model, 0.6 cut-off was optimal (AUC: 0.87 versus 0.85 [0.7 cut-off], 0.81 [0.8 cut-off]). The Trial-End timepoint yielded the most accurate prediction (AUC: 0.92 versus 0.87 [Start], 0.85 [Mid]). Despite <span><span>NCT03351244</span><svg><path></path></svg></span> not meeting the primary endpoint, a reduction in risk of first relapse with BI 409306 versus placebo was observed when analyzed with adherent completers (≥80 % across trial; HR = 0.485) and patients with predicted adherence ≥60 % (HR = 0.510).</div></div><div><h3>Conclusions</h3><div>Adherence data with longer monitoring durations (14 days), lower adherence cut-offs (0.6), and later timepoints (Trial-End) produced most accurate adherence predictions. Accurate adherence prediction provides insights about medication adherence patterns that may help clinicians improve individual adherence.</div></div>","PeriodicalId":21417,"journal":{"name":"Schizophrenia Research","volume":"275 ","pages":"Pages 42-51"},"PeriodicalIF":3.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142786835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}