Acta Psychiatrica Scandinavica

BackgroundBipolar disorder (BD) is frequently underdiagnosed, particularly in patients presenting with depressive disorders, leading to delays in appropriate treatment. Artificial intelligence (AI) applied to electronic health records (EHRs) may improve early detection by identifying clinically relevant symptom patterns. ObjectiveTo evaluate the diagnostic performance of a natural language processing (NLP)-based AI model for detecting BD-related features in EHRs of patients with affective diagnoses. MethodsA retrospective diagnostic accuracy study was conducted using 500 EHRs from a psychiatric referral hospital in Bogota, Colombia (2020-2024). The model extracted 18 predefined clinical domains from unstructured text and classified patients into four risk categories. Diagnostic performance was assessed in a validation subset of 100 records using independent psychiatric evaluation as the reference standard. Sensitivity, specificity, positive and negative predictive values, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) were calculated. ResultsThe model achieved high agreement in symptom extraction (mean 91.1%). Sensitivity was 96.4% (95% CI: 87.7%-99.0%) and specificity was 84.4% (95% CI: 71.2%-92.3%), with an F1-score of 0.92 and an AUC-ROC of 0.932 (95% CI: 0.881-0.975). A substantial proportion of patients with depressive diagnoses were identified as having confirmed BD or clinically relevant risk. The model analyzed complete EHRs 120 times faster than human reviewers. ConclusionsNLP-based analysis of EHRs can achieve clinically meaningful performance in identifying BD-related patterns while substantially reducing review time. The model may be useful as a clinical decision support tool for earlier identification of bipolar disorder.

BackgroundMental health systems face escalating demand that exceeds clinician capacity, making accurate severity-based triage a critical bottleneck. Severity assessment guides treatment intensity, resource allocation, and risk management, yet most clinically relevant information remains embedded in unstructured electronic health record (EHR) narratives, limiting its utility for scalable decision support. ObjectivesThis study evaluates whether a single large language model (LLM) can autonomously extract clinical factors from psychiatric EHR narratives, derive predictive weights from those factors, and use the resulting structured representation to predict clinician-implied severity at scale. MethodsFrom a Mayo Clinic repository of more than 2.7 million encounters, 15,000 de-identified psychiatric notes were sampled into a 5,000-patient discovery cohort and a 10,000-patient replication cohort. The same LLM (Llama 3 8B Instruct) extracted 17 background clinical factors and 3 treatment-action factors from each note. Severity reference labels were derived from the treatment-action factors using pre-specified clinical criteria. The LLM independently derived two factor-weight dictionaries from the discovery cohort: one capturing risk-oriented predictors of severe presentations and one capturing protective predictors. Five weighting conditions were then evaluated against the severity labels: the two LLM-derived dictionaries, two controls (LLM-derived variables with randomized weights; clinically irrelevant variables with arbitrary weights), and an unweighted zero-shot baseline. Performance was assessed across 928 valid iterations in the replication cohort. ResultsLLM-derived structured conditions significantly outperformed all controls and the baseline, with statistically equivalent performance between the two structured conditions. Improvements in precision and recall were balanced, indicating gains in discriminative capacity rather than threshold shifts. The variables and weights the LLM derived as predictors of severe presentations aligned closely with established clinical determinants of psychiatric severity. ConclusionA single LLM can derive clinically meaningful factor weights from unstructured EHR narratives and use them to predict psychiatric severity at scale, supporting a viable path toward interpretable, scalable triage in resource-constrained mental health systems.

Introduction: Youth mental health presentations are largely heterogenous, making it difficult to match individuals to the most appropriate interventions. Personalised, measurement-based care has the potential to improve clinical decision-making and support shared decision-making, but remains challenging to implement in routine practice. Advances in digital monitoring and causal modelling offer new opportunities to identify individual-level processes driving mental health difficulties and to generate personalised decision-support. This pilot study aims to evaluate the feasibility and acceptability of the Minding Your Mind computational decision-support approach, a newly developed approach integrating routine outcome monitoring, individual-level causal modelling, and personalised feedback to support shared decision-making between young people and their clinicians. Methods and analysis: The study involves two phases. Phase 1 will recruit young people aged 15-25 years and mental health clinicians to participate in workshops to co-design the decision-support approach and its implementation into routine practice. Phase 2 is a prospective, single-arm feasibility study involving young people receiving mental health care and their treating clinicians. Primary outcomes include feasibility, acceptability, appropriateness, and usability of the decision-support approach, assessed via self-report and objective process indicators. Secondary outcomes include changes in use and experiences with shared decision-making, and clinical and functional outcomes. Quantitative analyses will be primarily descriptive, with exploratory pre-post comparisons and sensitivity analyses. Qualitative interviews will explore user experiences and implementation barriers and facilitators. Ethics and dissemination: This study has been approved by the Sydney Local Health District (RPAH Zone) Human Research Ethics Committee (X25-0341). All participants will provide informed consent prior to participation. Findings will be disseminated through peer-reviewed publications, conference presentations, and accessible summaries co-developed with young people with lived experience.

Background Escalating mental health service demands have created a need to better identify young people most likely to require continued support from mental health services at the transition between childhood and adulthood. Anxiety is the most common adolescent mental health condition, yet its clinical significance and prognosis are not well understood. We aimed to examine the risk of young adult-onset psychiatric disorders in individuals with an adolescent anxiety disorder, and identify stratifiers of risk of subsequent psychiatric disorders in this group. Methods Individuals from the Norwegian Mother, Father, and Child Cohort Study (MoBa) with linked health records and aged 18 or over as of the 31st December 2023 were included. Those diagnosed with any ICD-10 anxiety disorder when aged 10-17 years were defined as having an adolescent anxiety disorder (n=2107, controls n=47,582). Polygenic scores (PGS) for psychiatric and neurodevelopmental conditions were calculated using LDpred2. Anxiety, comorbidities, and parental psychiatric history were defined through linked ICD-10 diagnoses. Sex was defined through linked records. Individuals were defined as having a young adult-onset psychiatric disorder if they first received any new psychiatric diagnosis aged 18-24. Results Adolescent anxiety diagnosis was associated with increased risk of all adult-onset psychiatric disorders (HR= 2.33-8.65). Post-traumatic stress disorder PGS, parental history of severe mental illness, and female sex were associated with increased risk of transition to a young adult-onset psychiatric disorder in people with an adolescent anxiety disorder. Conclusions Adolescent anxiety greatly increases the risk of a psychiatric disorder during the transition to adult life. Clinicians should consider female sex and parental psychiatric history when prioritising young people with anxiety for adult mental health service support. Future research needs to further consider whether polygenic scores would aid risk stratification in clinical practice.

ObjectiveHow structured clinical features and cluster-semantic embeddings interact under self-distillation in EHR prediction models is unknown. Existing approaches treat these sources separately (gradient-boosted trees exploit tabular features while sequence models process text), and their interaction under self-distillation regularisation remains uncharacterised. We introduce the Narrative Velocity (NV) framework and evaluate this interaction in a 7-model benchmark. Materials and MethodsCadence is a [~]5.86M-parameter residual multilayer perceptron (MLP) combining structured EHR features with frozen PubMedBERT embeddings of cluster-label strings under born-again self-distillation from a prior Cadence checkpoint (seed-42 teacher; [1]). Cadence is benchmarked against six comparators on MIMIC-IV v3.1 with dual-sex TRIPOD+AI reporting (5 student seeds for Cadence; 2-3 seeds for baselines). ResultsAt full-cohort scale, Cadence achieves 38.04 {+/-} 0.04% male and 35.66 {+/-} 0.04% female top-1 accuracy, exceeding the strongest non-neural baseline (XGBoost-2420, trained on the identical 2,420-dimensional input) by +1.35 pp male and +0.82 pp female (paired t-test on shared seeds 42-44: t(2) = 69.06, p = 2.10 x 10-4 male; t(2) = 25.32, p = 1.56 x 10-3 female). On time-to-next-event regression Cadence lowers MAE by 7.68 d male and 7.30 d female versus XGBoost-2420; FT-Transformer attains the lowest absolute MAE at full scale (27.58 d male, 36.63 d female), revealing a classification-regression trade-off across model families. A controlled 2 x 2 random-vector ablation isolates the self-distillation-embedding interaction at +0.49 pp top-1 (95% CI [0.35, 0.64] pp; bootstrap, n = 10,000 resamples; 3-teacher-seed mean +0.513 {+/-} 0.010 pp) under a matched-dimensionality null. A 3-teacher-seed validation (multi_teacher_02) confirms the interaction is robust to teacher-seed identity (per-seed values +0.525, +0.509, +0.507 pp; mean +0.513 {+/-} 0.010 pp). Cadence achieves the best Brier score among evaluated models (0.774 male / 0.798 female) but its raw probabilities are systematically miscalibrated (ECE 0.077 vs. XGBoost-884s 0.010); after a single scalar temperature scaling step (T * {approx} 0.81), ECE drops to {approx}0.028 while Brier remains best. On a small (n = 1,120 patients, 39,120 events) external OCR-extracted BWH cohort, Cadence ranked 3rd of 7 models with three confounded sources of error (institutional shift, OCR noise, centroid mapping); we therefore report this as a generalisation probe rather than a definitive external validation. At the longer h30 evaluation horizon Cadences MAE advantage reverses (47.35 d versus XGBoost 45.06 d), reflecting the absence of a matched-horizon self-distillation teacher. DiscussionThe 2 x 2 random-vector ablation confirms that the self-distillation gain on PubMedBERT embeddings (+0.78 pp) exceeds that on matched-dimensionality random vectors (+0.29 pp) by +0.49 pp, isolating the interaction to semantic content rather than feature dimensionality. The factorial decomposition (+0.49-0.51 pp interaction) and the sequential pipeline-level decomposition (Supplementary Table S3) are complementary triangulations under different reference frames and are not directly additive. ConclusionThis 7-model benchmark establishes a dual-sex, dual-metric, cross-institutional reference for next clinical event prediction under the TRIPOD+AI reporting framework. These results characterise discrimination and calibration on a single retrospective cohort; prospective evaluation, decision-curve analysis, and harm-benefit assessment are required before clinical deployment.

Objective: To test whether two brief mania measures, the Parent General Behavior Inventory-10 Mania form (PGBI-10M) and 7-Up, retain useful psychometric properties in a large population cohort, and to evaluate whether the PGBI-10M can identify Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS)-defined bipolar spectrum disorders in that setting. Method: Analyses used 11,000+ youths across late childhood and early adolescence from the Adolescent Brain Cognitive Development (ABCD) Study. For both PGBI-10M and 7-Up, we estimated descriptive statistics, internal consistency, confirmatory factor models, graded response models, and measurement-based care benchmarks (minimally important difference, reliable change, and clinical cutpoints). For the PGBI-10M, receiver operating characteristic (ROC) analyses estimated concurrent classification accuracy for bipolar diagnoses at baseline and 2-year follow-up and compared area under the curve (AUC) values with prior outpatient and community mental health samples. Results: Scores were lower than in clinical samples, but both measures remained psychometrically sound. The PGBI-10M showed alpha=.87-.88 and omega=.88; the 7-Up showed alpha=.78 and omega=.79. Longitudinal analyses indicated threshold differences across waves, likely reflecting caregiver recalibration and developmental changes, with modest impact on estimates. ABCD-based benchmarks supported meaningful and reliable change. The PGBI-10M discriminated bipolar cases (AUC=0.68 baseline; 0.77 follow-up), though performance was lower than in clinical samples. Positive predictive values were low in this population. Conclusion: The PGBI-10M and 7-Up support monitoring of manic and mixed symptoms, but the PGBI-10M alone is insufficient for universal bipolar screening. Brief mania scales are best used for targeted assessment and longitudinal monitoring within multi-informant workflows.

Background. Something in discourse with a person experiencing psychosis often "feels off" before formal assessment is completed, yet this disturbance has not been quantified at the level of ongoing dyadic conversation. Prior work has largely treated patient speech in isolation, limiting our capacity to measure how communicative disruption emerges within clinical exchange. Methods. We applied a three-level decomposition of conversational alignment in 109 patients with psychotic disorders (26 female) and 60 healthy controls (22 female) at baseline and 12 months (n = 115). Register divergence (dAUCnorm) captured lexical distance between interviewer and patient; embedding-based synchrony (rembed) measured semantic trajectory coupling; within-speaker coherence was computed separately for each speaker. We used linear mixed-effects models adjusted for timepoint and participant clustering. Results. Patients showed significantly greater lexical-semantic divergence from the interviewer (d = 0.48, p < .001) and reduced embedding-based synchrony (d = -0.59, p < .001), both effects replicating at each time point. Critically, the interviewer's within-speaker coherence was reduced during conversations with patients (d = -0.33, p = .016), indicating that the disruption extends beyond the patient to the interaction itself. Register divergence tracked impoverished thinking and synchrony tracked disorganized thinking (both FDR-corrected q = .038). Group differences were persistent at 12 months, indicating a partially stable profile. Conclusions. Conversational alignment in psychosis reveals a dyadic failure of semantic coordination that destabilizes the interviewing clinician's coherence even when patient narrative continuity is preserved. These transcript-derived alignment metrics offer a scalable approach to quantifying interpersonal communicative function from routine clinical encounters.

Background Treatment guidelines for borderline personality disorder (BPD) recommend assessment, diagnosis, and psychoeducation. We report on the feasibility and safety of a randomized controlled trial protocol of online psychoeducation, assessment, and personalized feedback as an immediate first step of care for BPD. Methods Newly diagnosed participants were randomized to receive 10 videos about BPD or general mental health for two weeks. Half the participants receiving BPD videos were randomized to receive personalized feedback on changes in symptom ratings and cognitive performance. Ecological momentary assessment (EMA) evaluated interpersonal interactions, emotions, and behaviors for 30 days. BPD symptoms, depression, and personality functioning were assessed at baseline, after videos, after feedback, and one month later. Results Eighty-two participants were randomized into three conditions that did not differ significantly in terms of demographics or baseline variables. Dropout occurred for 32.9% of the sample. No differences in rate of emergency room visits, hospitalizations, or other escalations in level of care were reported among groups. Satisfaction was higher for those receiving psychoeducational videos about BPD. Improvement in BPD knowledge in the psychoeducation conditions was significantly greater than the control condition. No statistically significant differences were found regarding reduction of BPD symptoms. The psychoeducation with feedback arm showed significantly greater improvements in self-impairment compared to controls with medium effect size at the final timepoint. Modeling of the relationship between time spent alone and BPD symptoms showed a positive correlation in the control condition, but in the group receiving both psychoeducation about BPD and feedback, this relationship was negative. Conclusion Online psychoeducational videos and assessment were safe, feasible, and acceptable to participants with newly diagnosed BPD. Psychoeducation with personalized feedback appears to be more effective than either BPD or general psychoeducation alone in improving deficits in self-functioning, which may relate to an increased capacity to be alone with fewer symptoms. The protocol was registered with ClinicalTrials.gov (NCT05358925, https://clinicaltrials.gov/study/NCT05358925) on April 28th, 2022.

Background: Dissociative experiences encompass a variety of discontinuities in awareness and perception that are elevated in the dissociative disorders and associated with extensive comorbid symptomatology. Accumulating evidence points to developmental trauma and trait responsiveness to verbal suggestions (REVS) as factors that confer risk for severe dissociative symptoms, but they have typically been studied in isolation. This study integrated these measures using prediction modelling to better understand their predictive value for the risk of dissociative psychopathology. Method: 1,104 non-clinical participants completed measures of trauma, dissociation and trait REVS. The predictive model was developed using elastic net logistic regression, internally validated with 10-fold cross-validation, and assessed using receiver operating characteristic (ROC) curve and area under the ROC (AUROC). Variables entered into the model were components of REVS, trauma, age, and their interactions. Results: A dissociative psychopathology at-risk group (7%) was characterised by younger age, greater trauma and elevated REVS, particularly involuntariness during cognitive-perceptual suggestions. The prediction model retained nine of ten predictors, with an AUROC of .77 [95% CI: .73, .82], reflecting good discrimination with moderate sensitivity (78%) but modest specificity (67%). Conclusions: These findings reinforce trauma and trait REVS as risk factors for dissociative psychopathology and demonstrate that they can be integrated in a model that can identify at-risk individuals. Further validation and extension of the model is necessary to improve the identification of individuals at risk for severe dissociative symptomatology and the diagnosis of dissociative disorders with implications for outcome trajectories.

Objective: Neuropsychiatric presentations are common across neurological and mental health services but they are often inadequately covered by core clinical psychology and clinical neuropsychology training. Consequently, we aimed to identify components for a neuropsychiatry curriculum for clinical psychologists using a Delphi process. Method: We completed a three-round e-Delphi study with 19 experts (clinical psychologists, neuropsychologists, psychiatrists, neurologists, individuals with lived experience of neuropsychiatric disorders). Round 1 collected ratings on 80 syllabus items derived from textbook reviews, conference topics, and a scoping review of neuropsychiatry syllabuses. Items failing to reach consensus were refined, and new topics added via free-text suggestions. Rounds 2 and 3 repeated rating and thematic analysis, culminating in a consensus meeting where items were classified as core or supplementary. Consensus thresholds were set at mean>=2.0, mean distance from the mean<=0.2, and => 75% agreement for final decisions. Results: The process yielded 40 core and 38 supplementary syllabus items. Core topics include autoimmune and neuroinflammatory disorders, delirium, functional neurological disorders, neuropsychiatric sequelae of epilepsy, stroke, traumatic brain injury, dementia, and multidisciplinary working, among others. Supplementary items covered background knowledge of less frequent but still prevalent disorders as well as competencies in interpreting clinical data alongside conceptual and historical issues. The final component list reflects both clinical competencies and emerging areas of practice, emphasising assessment, formulation, psychological interventions, cultural considerations, and medicolegal aspects. Conclusions: The e-Delphi derived curriculum provides a framework for neuropsychiatric competencies for postgraduate psychology training with modification needed for application in diverse healthcare settings.

Abstract Introduction: Over 30% of adults with Attention-Deficit/Hyperactivity Disorder (ADHD) show an insufficient response to standard pharmacological treatments, which underscores the need for evidence-based alternative interventions. Methods: In this sham-controlled study, 30 adult outpatients with ADHD were randomized to 12 weeks of active or sham transcranial direct current stimulation (tDCS) as add-on to a digital cognitive behavioral therapy application (dCBT app). Participants received either active (2 mA, 20 min/day, 5 days/week) or sham tDCS with anodal (left) and cathodal (right) stimulation applied over the dorsolateral prefrontal cortex (DLPFC). In parallel, access to the dCBT app was provided for three months. ADHD symptoms were measured before and after treatment and after a three-month follow-up using the Adult Self-Report Scale (ASRS v1.1). Results: All scales showed an improvement over time with medium-to-large within-subjects effects (Cohens d: -.48 to -.75), irrespective of group allocation. Two additional sensitivity analyses including (1) participants with over 75% of planned (sham)-tDCS sessions and (2) those who logged into the dCBT app on at least 5 days (median split) confirmed results. Response was observed in 1/15 (6.7%) of the tDCS group and 2/15 (13.3%) of the sham-tDCS group, with no difference between groups (p = .543, phi = -.111). Compliance to (sham-)tDCS was high. tDCS usability was rated marginally lower in the tDCS group. Conclusions: tDCS as an add-on therapy could not produce additional improvement in ADHS symptoms. The results are discussed in terms of contextual and patient-related aspects. ClinicalTrials.gov Identifier: NCT06766214.

BackgroundCommercial dental artificial intelligence in 2026 is over-whelmingly diagnostic: caries, calculus, periapical, and bone-level detection on radiographs. The clinically harder question that follows every diagno-sis -- given a patients chart and most recent procedure, what should the dentist do next -- remains unsolved at general-dentistry scale. The closest published system, MultiTP (Chen et al., 2024), is a CNN-RNN restricted to partial-edentulism cases and provides neither calibrated uncertainty, structured rationale, nor an evaluation that treats the model as decision support rather than as an autonomous classifier. MethodsWe introduce DentaCoPilot, a recommender that, given a structured chart, returns (i) a calibrated top-K probability distribution over Current Dental Terminology (CDT) codes for the next procedure, (ii) a verbalised confidence label, (iii) an explicit abstain flag when context is insufficient, and (iv) a chartgrounded rationale. We compare four classical baselines (frequency bigram, TF-IDF + logistic regression, XGBoost, MultiTP-style CNN-RNN) and six large-language-model (LLM) variants (Claude Haiku, Sonnet + chain-of-thought, Sonnet + retrieval, Opus + chain-of-thought, Sonnet + classical prior, Opus + classical prior) on a synthetic chart corpus of 500 patients (1,284 test examples). All LLM inference is routed through the local Anthropic Claude Code CLI; every call is logged for full audit. ResultsOn apples-to-apples evaluation, classical baselines reach 0.567 top-1 / 0.967 top-5; pure LLM variants trail at 0.267-0.467 top-1. Prompt-conditioning a Sonnet LLM on the classical baselines top-10 candidates (M5) closes the gap: top-5 rises from 0.733 (pure Sonnet + chain-of-thought) to 0.933, matching classical baselines, while preserving rationale and abstention. Increasing the LLM backbone from Sonnet to Opus does not improve accuracy with or without priming. Calibration via temperature scaling and coverage-risk analysis is reported for the baselines. ConclusionPrompt-conditioning a small LLM on a classical baselines top-K is the most cost-effective LLM design we tested for next-procedure recommendation, and the design preserves the augmentation features that distinguish the system from an autonomous classifier. A pre-registered clinician-in-the-loop evaluation at the KLE Vish-wanath Katti Institute of Dental Sciences (Belgaum, India) and a real-data evaluation on the multi-institutional BigMouth dental data repository are the next stage of work.

ObjectiveTo charactertise emergency department (ED) use among young people with bipolar disorder (BD) and compare patterns to those observed in anxiety, depressive, and psychotic disorders. Design, setting and participantsData linkage study using administrative ED presentation records (January 2020 to October 2020) and a transdiagnostic youth mental health cohort of 2243 individuals aged 12-30 years in New South Wales, Australia. Main outcome measuresED presentation patterns (any presentation, frequency, and rates) and reasons for presentation (mental health-related and non-mental health-related). ResultsOf the 354 young people with BD, 309 (87.3%) presented to an ED at least once. ED presentation rates were higher for BD than for anxiety (incidence rate ratio [IRR]=1.82, p<.001) and depressive disorders (IRR=1.32, p<.001), but similar to psychotic disorders (IRR=0.91, p=.379). Differences were primarily driven by mental health-related presentations. Recurrent mental health presentations were associated with illness progression (clinical stage and functional impairment) rather than diagnosis. However, the likelihood of mental health-related presentations remained higher in BD compared with anxiety and depressive disorders after adjustment. ConclusionsYoung people with BD have high rates of ED use, comparable to those with psychotic disorders. Although mental health-related presentations are more common in BD than in anxiety and depressive disorders, recurrence is largely explained by markers of illness progression. These findings highlight the need for community-based services that provide continuous and coordinated care for young people with complex mental health needs.

Background: The incidence of antidepressant withdrawal reactions in longer-term users and the influence of dosage is insufficiently understood. Objectives: Informed by neuropharmacological models and user surveys, this study examined symptom change during tapering and if increases were specifically associated with reductions below 75% of the minimum effective dose. Design: This was a prospective longitudinal cohort study with seven assessments over six months. Methods: Altogether 32 Swiss adult primary care patients who were on antidepressants for at least six months and in stable remission were assessed at baseline (week 0) before they started tapering and after 2, 4, 6, 8, 16, and 26 weeks. Withdrawal symptoms were measured repeatedly using an adapted version of the Discontinuation-Emergent Signs and Symptoms Scale (DESS) and the main outcome was intra-individual symptom change during intervals. Antidepressant dose was standardized relative to the minimum effective dose in the treatment of depressive and anxiety disorders. Results: Across intervals, reductions below 75% of the minimum effective dose were associated with symptom increases, while reductions above that threshold or no reductions were associated with symptom decreases. After adjusting for potential confounders, the rate of clinically relevant symptom increases contingent on dose reductions below 75% of the minimum effective dose was 33%, as compared to 13% during intervals with no dose reductions (OR=3.2, 1.4 to 7.4). We thus estimated that 60% of the risk of clinically relevant symptom increases was attributable to pharmacological withdrawal effects. The adjusted incidence rates for clinically relevant and severe withdrawal reactions were 32% and 11%, respectively. Conclusions: Consistent with neuropharmacological research findings, we found that antidepressant withdrawal symptoms emerge mostly following reductions below 75% of the minimum effective dose, affecting about one-third of patients. Even small reductions may trigger clinically relevant withdrawal reactions in this lowest dose-range, stressing the need for personalized tapering plans.

Background: While growing enthusiasm for the therapeutic potential of classic psychedelics has led to a rise in non-clinical use, attention to persisting adverse effects has emerged with delay. A subset of individuals reports persisting complications such as hallucinogen persisting perception disorder (HPPD), depersonalization/derealization disorder (DDD), anxiety and depression. Yet few medical services are equipped to address these complications. Aims: This qualitative study examines how societal, medical, and media discourses shape the experiences of individuals with persisting psychedelic-related complications, focusing on help-seeking trajectories. Methods: Thirteen semi-structured interviews with adults experiencing persisting psychedelic-related psychological symptoms (four women, nine men, age 19-49 years; HPPD (n = 10), DDD (n = 6), depression (n = 1), and anxiety (n = 1)) were conducted within a larger study on these complications. Data were analysed using reflexive thematic analysis. Reporting followed the COREQ guidelines. Results: Three interrelated themes emerged: (1) The dissonance between expectation and harm - idealised media and scientific portrayals of psychedelics shaped initial use and complicated recognition of adverse outcomes; (2) Stigma, silence, and self-blame - prohibitionist discourse and internalised shame significantly inhibited help-seeking; and (3) Between systemic absence and self-organised support - participants encountered clinical unpreparedness and epistemic dismissal, which often led them to rely on online peer communities and self-management strategies. Positive clinical encounters, characterised by professional expertise and nonjudgmental engagement, were experienced as helpful. Conclusions: Adequate clinical and conceptual frameworks for persisting psychedelic-related complications are lacking. An interdisciplinary, experience-informed approach integrating realistic risk communication, clinician training, and destigmatisation is required to support affected individuals.

BackgroundMost studies seeking to identify youth at increased risk for depression have developed prediction models using a limited set of risk factors in general population samples. It is unclear whether these models generalize to high-risk youth. Here, we developed machine learning algorithms to predict first-onset depression in youth from the general population and high-risk youth with attention-deficit/hyperactivity disorder (ADHD). MethodsParticipants were 4803 unrelated children from the ABCD study with no prior mood disorder and complete data at baseline (age 9-10 years) and 2-year follow-up. Support Vector Machine, Random Forest, and Elastic Net models were used to predict first-onsets from clinically-relevant risk factors spanning mental and physical health, cognitive, dispositional, interpersonal, and socio-environmental domains. Predictive performance was evaluated in the full sample and separately in participants with ADHD (N=584, 12.16%). ResultsModels trained on the full sample achieved good discriminative predictive power (area under the curve [AUC]=0.70 and accuracy=0.70-0.82). Predictors that replicated across models included earlier pubertal development, higher behavioral inhibition and aggression, and more time spent passively watching media content. In the ADHD subsample, model performance declined (AUC=0.46-0.61) and predictors only partly overlapped with those identified in the full sample. ConclusionsModels effectively predicted depression in the general population but showed poor generalization to high-risk youth with ADHD, suggesting different risk factors in this group. These findings highlight that models trained in general population samples may not generalize to high-risk groups, pointing to the need for more tailored efforts to predict depression in youth at increased risk.

PurposeIn clinical practice, accurate prediction of disease risk must be accompanied by transparent, human-understandable explanations to support diagnostic confidence, guide therapeutic decisions, and meet ethical and regulatory standards. While deep neural networks achieve high predictive performance in tasks such as cancer detection and diabetes risk stratification, their black-box nature prevents clinicians from understanding the reasoning behind predictions, severely limiting trust and safe integration into patient care. MethodsWe present Regression-Based Boolean Rule (RBBR), a framework that automatically derives clinically interpretable Boolean rules directly from patient data. RBBR generates human-readable conjunctions (logical AND combinations) of up to three clinical features, transforms them into inputs for ridge regression to predict binary or multi-class disease outcomes, estimates rule importance via regularized coefficients, and selects the most parsimonious and predictive rule sets using the Bayesian Information Criterion. ResultsApplied to six real-world medical datasets (lung cancer screening and staging, Wisconsin and diagnostic breast cancer, heart failure, and early-stage diabetes risk), RBBR consistently produced concise, clinically meaningful rules - e.g., gender-specific symptom combinations in diabetes, distinct histopathological subpopulations in breast cancer, and symptom-risk factor interactions in lung cancer - with strong explanatory power (R2 up to 0.92) and competitive discrimination. ConclusionBy delivering logical, transparent decision rules aligned with clinical reasoning (if symptom A and B, then high risk), RBBR bridges the gap between predictive accuracy and bedside usability, enabling clinicians to validate predictions, identify high-risk patients, stratify subpopulations, and enhance shared decision-making in routine care.

Introduction: Engagement with mental health services (MHCS) during the first episode of psychosis (FEP) is critical for symptom control, quality of life, and relapse prevention. However, disengagement rates remain high in Uganda with severe consequences for patients and caregivers. This study protocol describes a mixed-methods investigation which aims to examine the relationship between patients and caregivers lived experiences and mental health service engagement during first-episode psychosis. Methods and Analysis. The mixed-methods study will recruit 82 patients with first-episode psychosis and their primary caregivers from Butabika National Referral Mental Hospital in Kampala, Uganda. Inclusion criteria are ages 18-60, less than 12 weeks on antipsychotic medications, living in the greater Kampala Metropolitan Area, with a consenting caregiver. Caregivers must be an adult (> 18years) providing full-time care for at least 6 months prior. Patients with substance use disorders will be excluded. Qualitative data on the lived experiences of patients and caregivers will be collected using the draw-write-and-tell method, while quantitative data on service engagement and associated factors will be collected using semi-structured questionnaires. The data will be analysed using Stata version 18, and participants will be reimbursed for their time. Ethics and Dissemination. Ethical clearance has been obtained from the School of Medicine Research and Ethics Committee (SOMREC) Ref: Mak-SOMREC-2024-1002 and institutional approval from Butabiika National Referral Mental Hospital. All participants will provide informed consent prior to participation. Data will be de-identified and securely stored, with results disseminated through peer-reviewed academic publications, conferences and community stakeholder workshops.

BackgroundScreen use is pervasive in childhood and adolescence, yet its role in antisocial behaviour (ASB) remains uncertain. While cross-sectional studies consistently link higher screen use to elevated ASB, longitudinal evidence is mixed, and few studies have controlled adequately for prior behaviour and genetic liability. Thus, it remains unclear whether these associations reflect prospective influences of screen exposure, or underlying vulnerabilities shared with ASB. We investigated whether screen use is a modifiable risk factor or a marker of underlying vulnerability. MethodsWe analysed data from up to 41,562 children in the Norwegian Mother, Father, and Child Cohort Study (MoBa). ASB traits and ICD-10-based conduct disorder (CD) diagnoses were assessed at ages 5, 8 and 14 years, together with screen use (total exposure and modality). Cross-sectional logistic regression models examined associations between screen use and ASB traits/CD at each age, adjusting for sex and parental education. Polygenic risk scores for ASB (PRSASB) were used to assess genetic susceptibility and gene-environment interplay. Lagged logistic models tested whether screen use predicted later ASB, adjusting for prior ASB. Linear mixed-effects models examined developmental patterns across age. ResultsHigher screen use was positively associated with ASB traits and CD across all ages, with dose-response patterns across screen-use modalities. Social media showed the strongest modality-specific association at adolescence. In lagged models, screen use did not predict later ASB after adjustment for prior ASB. Longitudinal models showed significant but attenuating associations across development. PRSASB was independently and additively associated with ASB outcomes but did not interact with screen use. ConclusionsWe found that higher screen use was consistently associated with antisocial outcomes across childhood and adolescence. However, the absence of prospective associations after accounting for prior behaviour, together with independent genetic contributions, suggests that screen use may be better understood as a marker of underlying vulnerability rather than an independent driver of antisocial development.

Background: Biopsychosocial factors associated with functional changes, including changes in personality, communication, movement, and weight, were evaluated in individuals with Down syndrome (DS) during the COVID-19 pandemic. Method: Caregivers of individuals with DS (aged [&ge;]12, n = 118) completed an online survey. Elastic net regression with bootstrap resampling assessed 31 candidate predictors. Results: Pandemic-related mental health was most strongly associated with functional changes ({beta} = 0.388). Healthcare access barriers were also reliably selected: inability to access mental health treatment, difficulty affording insurance, difficulty accessing specialists, and residing in a low-income health professional shortage area. The model explained 35.2% of variance. Conclusions: Mental health and healthcare access barriers were biopsychosocial correlates of functional changes for people with DS during COVID-19.