JAMA Pediatrics

Background Education outcomes predict life chances. However, poverty, ill-health and disability are barriers to achievement. We examined determinants of academic attainment of children with and without major congenital anomalies in state-funded mainstream schools at ages 11 and 16 (key stages [KS] 2 and 4). Methods and Findings Routinely collected electronic records for children born in Wales 01/01/1998-31/12/2007 until 31/12/2019 were linked in the Secure Anonymised Information Linkage (SAIL) Databank. Education outcomes were explored using logistic regression, adjusting for: anomalies, maternal and child deprivation, prescribing, hospitalisation, gestation length, childs sex, and special education needs (SEN) provision. Children with anomalies were less likely to achieve academic standards: however, attainment was more closely associated with affluence. At age 11, 81.87% (7167/8754) with and 93.80% (232,450/247,814) without anomalies passed (odds ratio [OR] 0.30, 95% confidence intervals [CI] 0.28-0.32). At age 16, 46.76% (2070/4427) with and 56.10% (69,732/124,300) without anomalies achieved 5 General Certificates of Secondary Education (GCSEs) at grades C-A* including English/Welsh, Maths and Science (EWMS) (OR 0.69, 0.65-0.73). Discrepancies narrowed in adjusted analyses, particularly when SEN provision was accounted: aOR 0.72 (0.66-0.78) at KS2, and aOR 0.93, (0.87-1.00) for 5 GCSEs C-A* with EWMS. These GCSEs were achieved by 29.65% (307/1034) children with anomalies and 38.42% (10,875/28,305) of unaffected children in the most deprived quintile{dagger}: in the most affluent quintile, figures were 67.57% (547/810) and 74.98% (16,978/22,644). Children with anomalies, receiving maximum SEN support, eligible for Free School Meals (FSM) were the least successful: 5/192 (2.6%) passed 5 GCSEs C-A* with EWMS, as did 37/354 (10.4%) ineligible for FSM. The strongest associations with these GCSEs were SEN statements (aOR 0.07, 0.06-0.07), FSM eligibility (aOR 0.39, 0.37-0.41), and epilepsy (aOR 0.60, 0.45-0.80). However, data were unavailable for 15-18% of children, mainly those educated outside mainstream schools, and some co-morbidities. Generalisation of findings to other countries rests with readers. Conclusions Many children with anomalies from affluent households succeeded. The children left behind lived with poverty and ill-health from congenital anomalies and/or epilepsy. SEN provision mitigated the impact of disadvantage, but poor children with anomalies were unlikely to succeed. {dagger}taking maternal Welsh Index of Multiple Deprivation (WIMD) 2014 at birth.

Objective: To evaluate whether a locally hosted open-weight large language model (LLM) can extract documented psychosocial factors from pediatric psychiatric intake notes and apply validated extraction to a large emergency psychiatry cohort. Materials and Methods: We identified emergency department presentations at Cincinnati Children's Hospital Medical Center from January 1, 2016, through December 31, 2024, among patients younger than 18 years with psychiatric billing diagnoses. Using full-text intake notes, gpt-oss:120b classified peer conflict, sleep disruption, and school-related academic, attendance, and disciplinary issues as detected, negated, or indeterminate. Four human raters independently reviewed 50 notes. We compared Fleiss' kappa among humans alone versus humans plus the LLM, assessed repeated-query stability across 50 independent calls per note, and applied the workflow to all eligible notes. Results: Among 37,315 eligible admissions, 22,284 had eligible intake notes; 22,270 produced parseable JSON. In detected-versus-not-detected coding, human-plus-LLM reliability did not differ significantly from human-only reliability across measures (human {kappa} 0.71-0.94; human-plus-LLM {kappa} 0.70-0.93). Stability was associated with human agreement: mean LLM-human agreement increased from 42.6% for classifications with less than 80% stability to 82.7% for classifications with 100% stability (Pearson r = 0.36). Full-cohort extraction showed frequent and overlapping documented factors: sleep disruption was most frequently detected (57.7%), followed by peer conflict (47.2%), academic issues (43.4%), disciplinary issues (43.3%), and attendance issues (16.9%). Discussion: Agreement varied by construct and was strongest when repeated model outputs were stable. Conclusion: Locally hosted open-weight LLMs can support scalable structured extraction of documented psychosocial factors from pediatric psychiatric intake notes after local validation.

We assembled a multimodal clinical dataset describing demographics, placement history, prenatal substance exposure (PSE), birth characteristics, adverse childhood experiences (ACEs), International Classification of Diseases (ICD) diagnoses, and laboratory results for 3,685+ pediatric patients evaluated between 2014 and 2024 at the University of Minnesotas Adoption Medicine Clinic (AMC). Data were curated from electronic medical records through a combined manual and automated extraction protocol using a standardized operating procedure. The resulting dataset integrates structured EMR fields including neuropsychological, laboratory, and diagnostic information with manually pulled fields of ACE scores, PSE history, and placement history. We provide an overview of the population represented and describe the datasets structure, variable definitions, and validation procedures. This resource enables investigations into how early adversity impacts medical and developmental outcomes, and provides one of the largest standardized clinical placement history, PSE, and ACE datasets in an adoption and foster care pediatric population.

BackgroundChildren with cleft lip and/or palate (CL/P) experience increased rates of neurodevelopmental difficulties, including ADHD, autism spectrum disorder, and educational challenges. While rare neurodevelopmental copy number variants (ND-CNVs) are enriched in this population and associated with poorer outcomes, these variants are present in only a small proportion of children born with cleft. Whether shared common genetic variation contributes to neurodevelopmental comorbidities in CL/P remains unknown. MethodsWe investigated this question using data from 2,313 children with CL/P from the Cleft Collective and 7,913 population controls from the Millennium Cohort Study. We tested for shared genetic architecture using linkage disequilibrium score regression, examined associations between polygenic risk scores for eight cognitive, neurodevelopmental, and psychiatric traits and developmental and behavioural outcomes within the cleft population, compared polygenic risk scores between cases and controls, burden between ND-CNV carriers and non-carriers, and employed two-sample Mendelian randomization to test whether genetic liability to cleft causally influences neurodevelopmental outcomes. ResultsLinkage disequilibrium score regression revealed little evidence of genetic correlations between CL/P and any of the eight traits examined. Within the cleft population, polygenic risk scores demonstrated expected associations with developmental and behavioural outcomes; however, children with CL/P did not have increased polygenic risk scores for ADHD, autism, depression, anxiety, schizophrenia, bipolar disorder, or lower scores for educational attainment or intelligence compared to controls. Mendelian randomization provided no robust evidence that genetic liability to cleft causally influences neurodevelopmental outcomes. ND-CNV carriers did not differ from non-carriers in polygenic burden. ConclusionsThe increased neurodevelopmental risk observed in CL/P does not appear to be explained by shared common genetic architecture with psychiatric disorders, contrasting with established rare variant contributions. Polygenic risk scores for neurodevelopmental traits predict behavioural outcomes within the cleft population similarly to the general population, indicating these genetic factors operate independently of cleft status but remain clinically relevant.

OBJECTIVE: Anthropometric data are critical in paediatric care, routinely assessed during clinical visits, and available in electronic health records (EHRs). We describe the feasibility of extracting anthropometric data from heterogeneous EHR systems of Swiss childrens hospitals, evaluate their availability and quality, and assess the cohorts representativeness of the general population. METHODS: In this multicentre study (SwissPedGrowth), we retrospectively collected EHRs from patients <20 years who visited hospitals in Basel, Bern, Geneva, Lausanne, Luzern, St. Gallen, or Zurich between 2017-2023. Sociodemographic, administrative, and clinical information from EHRs were provided in a standardized way by a paediatric national data stream (SwissPedHealth), including the Swiss Neighbourhood Index of Socioeconomic Position (Swiss-SEP). We counted anthropometric recordings per visit to describe availability and used a self-developed and an existing (growthcleanr) algorithm to investigate data quality. To assess representativeness, we compared sociodemographic characteristics between SwissPedGrowth and the general paediatric population in Switzerland, computed standardized differences (effect size: 0.2 small, 0.5 medium, 0.8 large), and weighted the study population to reduce differences. RESULTS: We included 477,531 patients and 2,171,633 hospital visits; 54% boys, 71% Swiss, mean Swiss-SEP 65 (SD: 11), and median age at visit 6.3 [IQR: 2.3, 11.8] years. Height recordings were available for 20% of the visits, weights for 43%, and head circumferences for 5%, with better availability for inpatient stays than outpatient or emergency visits. Combining the self-developed and existing algorithm, 4% of heights and 3% of weights were flagged as outliers and 29% of heights and 31% of weights as carried forward from previous visits or same day duplicates. Sociodemographic differences between SwissPedGrowth and the general population were small or small-to-medium and disappeared after weighting. CONCLUSION: SwissPedGrowth demonstrates feasibility of extracting high-quality anthropometric data for paediatric growth research, but challenges regarding completeness and harmonization of EHR data across Swiss hospitals remain.

Introduction: While growing evidence suggests that music training supports child development, few long-term randomized controlled trials (RCTs) have rigorously tested these claims. Moreover, it remains unclear whether the benefits are confined to music-specific domains or extend to higher-order cognitive functions such as inhibitory control (IC), a core executive function associated with long-term outcomes in academic achievement, career success, socio-emotional health, and physical well-being. This paper presents the protocol for the Extracurricular Activity and Child Early Learning and Development (EXCEL) trial, an RCT designed to assess the feasibility of a long-term music training program focusing on the brain and behavioral correlates of IC. Methods: A total of 126 children, aged 6 to 8 years and residing in neighborhoods with limited resources in Los Angeles, were individually randomized to either a music (intervention) or theatre (active control) after-school program. Both programs were delivered over 24 months by established community arts organizations. Eligibility criteria included: average intellectual functioning, no major medical or psychiatric conditions, and MRI eligibility. Children with prior formal music training exceeding six months or severe hearing impairment were excluded. Before the intervention began, all participants completed baseline behavioral and neuroimaging assessments. The primary trial aim was to assess the effects of extended music training, relative to theatre training, on changes in measures of IC (i.e., Go/No-Go task and delayed gratification) and related neural functional activation. A secondary interim aim of the trial was to evaluate the feasibility of conducting a long-term RCT of music education in a first cohort, measured by participant retention, adherence to the program, willingness to continue at the 12-month mark, and fidelity. Progress: Recruitment, screening, baseline testing, randomization, and program enrollment began in August 2022, and after-school programming began in October 2022. The randomized interventions and all data for the first cohort (N = 42) have been collected. Intervention and active control programs for a second cohort are ongoing and will end in Fall 2026. Discussion: This paper reports the EXCEL trial protocol and provides feasibility estimates for implementing a long-term randomized controlled trial of music training in real-world, community-based settings with children. While similar neuroimaging RCTs are currently underway in Europe, the EXCEL trial is among the first in the United States to integrate longitudinal neuroimaging with arts intervention. Findings will inform the viability of scaling such programs and contribute to our understanding of how sustained music engagement may influence the development of inhibitory control circuitry in childhood.

ImportanceGuideline-concordant care for young children with attention-deficit/hyperactivity disorder (ADHD) includes recommending parent training in behavior management (PTBM) as first-line treatment. However, assessing guideline adherence through manual chart review is time-consuming and costly, limiting scalable and timely quality-of-care measurement. ObjectiveTo evaluate the accuracy and explainability of large language models (LLMs) in identifying PTBM recommendations in pediatric electronic health record (EHR) notes as a scalable alternative to manual chart review. Design, Setting, and ParticipantsThis retrospective cohort study was conducted in a community-based pediatric healthcare network in California consisting of 27 primary care clinics. The study cohort included children aged 4-6 years with [&ge;] 2 primary care visits between 2020-2024 and ICD-10 diagnoses of ADHD or ADHD symptoms (n=542 patients). Clinical notes from the first ADHD-related visit were included. A stratified subset of 122 notes, including all cases with model disagreement, was manually annotated to assess model performance in identifying PTBM recommendations and rank model explanations. ExposuresAssessment and plan sections of clinical notes were analyzed using three generative large language models (Claude-3.5, GPT-4o, and LLaMA-3.3-70B) to identify the presence of PTBM recommendations and generate explanatory rationales and documentation evidence. Main Outcomes and MeasuresModel performance in identifying PTBM recommendations (measured by sensitivity, positive predictive value (PPV), and F1-score) and qualitative explainability ratings of model-generated rationales (based on the QUEST framework). ResultsAll three models demonstrated high performance compared to expert chart review. Claude-3.5 showed balanced performance (sensitivity=0.89, PPV=0.95, and F1-score=0.92) and ranked highest in explainability. LLaMA3.3-70B achieved sensitivity=0.91, PPV=0.89, and F1-score=0.90, ranking second for explainability. GPT-4o had the highest PPV [0.97] but lowest sensitivity [0.82], with an F1-score of 0.89 and the lowest explainability ranking. Based on classifications from the best-performing model, Claude-3.5, 26.4% (143/542) of patients had documented PTBM recommendations at their first ADHD-related visit. Conclusions and RelevanceLLMs can accurately extract guideline-concordant clinician recommendations for non-pharmacological ADHD treatment from unstructured clinical notes while providing clear explanations and supporting evidence. Evaluating model explainability as part of LLM implementation for medical chart review tasks can promote transparent and scalable solutions for quality-of-care measurement.

BackgroundOutdoor play and limited screen time are critical for preschoolers physical health and socio-emotional development, yet little is known about how caregiver nativity and acculturation shape these behaviors. MethodsWe analyzed the 2022-2023 National Survey of Childrens Health data for 10,157 U.S. children 3-5 years old. Generalized linear models estimated associations between caregiver nativity and length of U.S. residence and childrens outdoor play and weekday screen time, adjusting for child, caregiver, and household covariates. Models tested interactions with race/ethnicity. ResultsOverall, caregiver length of U.S. residence was not associated with childrens outdoor play. However, screen time differed - children whose caregivers arrived Pre-1997 had lower odds of screen time frequency, whereas those whose caregivers arrived between 1997-2005 had higher odds compared with children of U.S.-born caregivers. Associations for weekday outdoor play and screentime varied significantly by child race/ethnicity. ConclusionsCaregiver length of U.S. residence appears more strongly related to preschoolers screen time than outdoor play, with notable differences across racial/ethnic groups. Culturally tailored strategies may be needed to reduce early childhood screen exposure and support healthy movement behaviors among immigrant families.

Background Although an expansive body of evidence exists on children's mental health during the COVID-19 pandemic, it is largely restricted to the early phases and lockdowns. This study examines longitudinal changes in child and youth mental health symptoms across two years of the COVID-19 pandemic, with data collection strategically timed to capture variability in pandemic restrictions. Methods A population-based longitudinal study of 1,261 children and youth aged 4-17 years followed prospectively from January 2021 to December 2022, with five waves of data collected in Ontario, Canada. Latent growth curve modelling was used to estimate trajectories of parent-reported mental health symptoms and identify baseline and time-varying covariates associated with variable trajectories. Findings Mental health symptoms were elevated and stable during lockdowns, followed by significant reductions as pandemic restrictions loosened, particularly for oppositional defiant and inattention/hyperactivity symptoms compared to internalizing symptoms. Children without pre-existing clinician diagnosed physical, mental or neurodevelopmental conditions and those not in lockdown at baseline demonstrated relative increases in mental health symptoms during lockdowns; and girls, compared to boys, demonstrated smaller reductions in internalizing symptoms as restrictions loosened. Concurrent and lagged associations between parental distress and children's mental health symptoms varied across the pandemic. Interpretation Variation in symptom trajectories by mental health domain, gender, pandemic restrictions and pre-existing diagnosed conditions underscores the need for tailored, equity-informed pandemic planning and response. Policies designed to optimize the balance between the need to reduce viral community transmission whilst limiting pandemic lockdowns may mitigate adverse impacts on child and youth mental health. Funding Ontario Ministry of Health

Background Large-for-gestational-age (LGA) and macrosomic newborns are at increased risk of adverse perinatal outcomes, including death, yet the burden of neonatal mortality associated with these conditions in low- and middle-income countries (LMICs), where ongoing nutritional and epidemiological transitions suggest their prevalence will rise, remains poorly quantified. In this study, we quantify the neonatal mortality risk associated with LGA and macrosomia from 16 subnational birth cohorts in low- and middle-income countries between 2000 and 2017. Methods and findings This is an individual-participant meta-analysis to estimate neonatal mortality rates (NMRs) and relative risks among LGA infants (>90th and >97th percentile birth weight-for-gestational-age using INTERGROWTH-21st) versus appropriate-for-gestational-age (AGA, 10th-90th percentile) infants. Macrosomic ([&ge;]4000 g and [&ge;]4500 g) neonates were compared with those weighing 2500 g-3999g. Missing birth weights were imputed using recalibration and multiple imputation methods. We used random effects meta-analysis to pool relative risks. Median prevalences of LGA >90th and >97th percentile were 5.3% (interquartile range 3.6-8.2) and 2.6% (IQR 1.3-4.5), respectively; macrosomia ([&ge;]4000 g and [&ge;]4500 g) prevalences were 1.0% (IQR 0.3-3.1) and 0.06% (IQR 0.0, 0.30), respectively. Mortality was highest among preterm plus LGA infants (61.3 per 1000). LGA infants in the >90th percentile had over twofold increased mortality compared with appropriate-for-gestational-age infants (RR: 2.46; 95% CI: 1.86-3.25), while >97th percentile infants had a higher risk (RR: 3.77; 95% CI: 2.50-5.69). Term LGA >97th percentile infants also showed elevated mortality (RR: 3.14; 95% CI: 1.58-6.22). For LGA >97th percentile, the risk was higher in the early neonatal period (RR: 2.71; 95% CI: 1.92-3.82) than late (RR: 1.69; 95% CI: 1.22-2.34). There was no overall association between macrosomia ([&ge;]4000 g) and neonatal mortality. Population attributable fractions were 7.2% for LGA >90th percentile and 0.4% for macrosomia ([&ge;]4000 g). Conclusions Neonatal mortality risks were elevated among LGA infants in low- and middle-income countries, particularly at extreme values (>97th percentile) and during the early neonatal period. Macrosomia showed weaker, less robust associations. Although LGA prevalence is currently low ([~]5%) and contributes less to neonatal mortality than small newborns, ongoing nutritional and epidemiological transitions suggest increasing prevalence. This highlights the need for strengthened surveillance, monitoring, and improved delivery planning to ensure that no population is left behind.

Despite autisms prominent genetic etiology and early-life origins, parsing genetic effects contributing to the condition into those that operate directly (via allelic transmission to offspring) vs. indirectly (via influencing prenatal environment) remains challenging. We examined this using a novel design leveraging 3-generation family linkage in Danish national registers. The cohort included all children born in Denmark from 1998-2015 and their relatives identified through 3-generation family linkage. The analytic sample comprised full maternal cousin pairs, including parallel (children of mothers sister) and cross cousins (children of mothers brother). Exposures were diagnoses in the index mother previously associated with offspring autism; the outcome was autism diagnosis in cousins of the index child. We used Cox proportional hazards models to estimate associations separately in parallel and cross cousins, followed by comparisons of these hazard ratios to infer mechanisms. Several maternal diagnoses (e.g., postpartum hemorrhage, personality disorders, epilepsy) were associated with autism in both parallel and cross cousins, consistent with shared direct genetic effects. Other conditions (e.g., false labor, recurrent major depressive disorder, other anxiety disorders, systemic connective tissue involvement) showed stronger associations in parallel than cross cousins, supporting additional indirect genetic effects operating through the prenatal environment. Adjustment for the same diagnosis in the cousins own mother did not substantially change estimates, providing no evidence for an additional role of non-genetic mechanisms associated with the diagnosis. These findings suggest that both direct and indirect genetic effects contribute to observed links between maternal health and offspring autism, highlighting etiologic heterogeneity and highlighting a registry-based family design to separate these pathways without genetic data.

AI-assisted clinical care may compound, rather than correct, existing health inequities. We applied Omar and colleagues' validated four-domain emergency-medicine benchmark to OpenEvidence (OE), a literature-grounded clinical LLM used by tens of thousands of US physicians daily, across 100 emergency-department cases and 20 sociodemographic labels. OE was consistent on the codified clinical decisions, triage, workup, and treatment, but diverged sharply on mental-health screening, where it flagged many historically marginalized groups between three and ten times more often than demographically unmarked cases. Cases labeled as unhoused received recommendations in 78 to 87 percent of responses (versus a 9 percent no-identifier-control rate); cases labeled as transgender in 22 to 24 percent; and Black transgender women specifically in 47 percent. A pre- registered audit of 193 free-text rationales localized the differential to the inner layer of the response, in the structure and tone of the rationale rather than the recommendation itself. Literature grounding may redistribute sociodemographic disparity in clinical AI rather than remove it. As clinical LLMs move toward agentic deployment, equity audits should examine how evidence is applied to each patient, not only whether citations are present.

Aim: To systematically evaluate evidence on the effects of post-discharge early developmental intervention programs (EI) on behavioral development, quality of life, participation, executive functioning, parent-child interaction, and use of medical services from infancy through adolescence in children born preterm. Method: Four bibliographic databases and one trial registry were systematically searched for randomized controlled trials up to April 23, 2024. Two reviewers independently screened studies and extracted data. In clinically and methodologically comparable studies, random-effects meta-analysis were performed. Risk of bias was assessed with the Cochrane RoB 2 tool, and certainty of evidence with the GRADE approach. Results: Twenty-six studies met inclusion criteria, eleven studies including 2,315 preterm born infants reported relevant outcomes, and seven contributed to meta-analyses. Most reported results showed some concerns or high risk of bias; certainty of evidence ranged from very low to moderate across outcomes. EI may offer small benefits for selective attention, behavioral problems and parent-child interaction. Little to no effect was found for special educational needs, language skills, executive functioning and the use of medical services. No included studies evaluated the effect of EI on ADHD, quality of life, or participation related to mobility or leisure activities. Interpretation: EI may improve problems typically seen in preterm children and should be offered especially to those with additional medical or social risk factors. High-quality, contemporary trials are needed to establish reliable clinical recommendations regarding EI strategies and complementary interventions throughout childhood.

BackgroundAn upsurge in Streptococcus pyogenes infections 2022-2023 highlighted potential benefits of point-of-care tests (POCT) to support clinical pathways, prevent outbreaks, and optimise antibiotic use. ObjectivesWe conducted a pilot research study in a west London paediatric emergency department (ED) to determine whether a molecular POCT had potential to alter management in children who were also having a conventional throat swab taken for culture. MethodsChildren <16 years presenting to ED who had a throat swab requested by a clinician were invited to have a second swab taken for research purposes only. Clinical management was unaffected by the research swab result, which was processed using a molecular POCT that was not approved for use in the host NHS Trust. ResultsPrevalence of streptococcal infection was low during the study (May 2023-June 2025); swab positivity in symptomatic children was 12.8% (6/47). Overall, 38/49 (77.6%) participants who had throat swabs received antibiotics. Of those children recommended to receive antibiotics, 29/38 (76.3%) had a negative POCT. Mean time to reporting of positive throat swab culture results was 3.67 days (range 3-5 days) leading to occasional delay in treatment, although POCT identified positive results within minutes. ConclusionAntibiotic use was frequent and could be avoided or stopped by use of a rule out POCT in over three-quarters of children in the ED, if suspicion of S. pyogenes is the main driver for prescribing. POCT were easy to process and produced immediate results compared with culture, in theory enabling timely decision-making and avoiding treatment delay.

BackgroundConversational AI safety systems are primarily evaluated using message-level content monitoring, which assesses inputs and outputs in isolation. This message-by-message approach can miss interaction-level risks that emerge over extended conversations, including patterns discussed in reports of "AI psychosis." Critically, by the time users express overt psychosis-spectrum content, opportunities for intervention may be limited. ObjectiveWe investigated whether LLM responses gradually expand and connect interpretations beyond the users original concerns, a process we term structural drift. We also tested whether this drift can be detected early and automatically. MethodsWe developed an automated, LLM-adapted rubric-based prompt for seven domains of anomalous (psychosis-spectrum) experience, derived from phenomenological psychiatry to capture subtle shifts in subjective interpretation. In Part 1, we evaluated the rubric using gold-standard text excerpts (N = 484) adapted from clinically validated qualitative instruments. In Part 2, we analyzed 1,290 user-LLM response exchanges from 7 dialogues, using 3 different LLMs (5 repeats each), to measure (i) domain amplification (increasing score within a domain) and (ii) domain expansion (new domains appearing over time). ResultsAutomated scoring showed strong agreement with gold-standard excerpts (domain accuracy 82.7-98.9%; exact 0-3 agreement 63.6-82.7%). Across dialogues, we observed significant amplification in four domains (p < .05; d = 0.14-0.46) and domain expansion in 83.8% of dialogues (88/105; p < .001). ConclusionsAI responses can systematically expand and intensify users descriptions beyond their initial input. Taken together with the predictive-processing accounts of psychosis, the exposure itself may reinforce maladaptive inferences. Because drift is detectable from ordinary dialogue without clinical-style probing, this structural drift detection may support scalable, real-time monitoring for emerging risks before overt escalation.

Background: Adverse childhood experiences (ACEs) are traumatic or adverse events in early life that can have lasting effects on behavioral, emotional, and psychological functioning. Prior research suggests ACEs relate to later psychiatric outcomes through threshold, cumulative, and individual-specific risk patterns. Few studies, however, have operationalized all three models to test ACE-specific associations with diagnosed psychiatric disorders in individuals who are adopted or with foster care histories. Methods: We conducted a cross-sectional retrospective study using electronic health record data from foster care and adopted patients aged 0-21 years old seen at the University of Minnesota Adoption Medicine Clinic (UMN-AMC) between 2014-2024. Extracted measures included ACE history, demographics, and psychiatric diagnoses. We used latent class analysis and logistic regression to identify clusters of adversity and estimate associations with psychiatric diagnosis domains, adjusting for Sex and Age at Initial Visit. Results: ACEs showed a threshold pattern across psychiatric domains, with higher ACE counts associated with greater odds of psychiatric diagnoses. Individual risk modeling indicated that exposure to abuse or violence was associated with higher odds of psychiatric diagnoses. Across cumulative and individual risk approaches, Anxiety Disorders, Mood Disorders, and Behavioral or Emotional Disorders showed the greatest sensitivity to adversity. Conclusion: Current ACE models may not fully capture neurodevelopmental impacts reflected in diagnosed psychiatric disorders among adolescents, particularly in high-risk groups such as foster and adopted individuals. In a large clinic sample our findings support a nuanced association between ACEs and later psychiatric diagnoses and highlight the need for ACE-focused assessment, prevention, and treatment strategies tailored to foster care and adopted populations.

Objectives To explore caregiver and clinicians perspectives on implementing mental health conversations and supports for caregivers of children with chronic conditions in paediatric outpatient clinics. Specifically, views were sought on (a) screening approaches and measures (phase 1) and (b) how feedback and support could be provided to caregivers experiencing mental health difficulties (phase 2). Methods Caregivers and clinicians from two outpatient clinics (neuromuscular and diabetes) at a tertiary paediatric hospital in Melbourne, Australia participated in online focus groups in July and August 2024. Caregivers were recruited from outpatient clinics and clinicians were recruited via email. Both groups were combined for phase 1 before separating into breakout rooms for phase 2. Two authors conducted reflexive thematic analysis of transcripts using NVivo. Results Sixteen participants (caregivers n = 8; and clinicians n = 8) took part in in two semi-structured focus groups. Analysis generated two overarching domains, each comprising multiple themes. Domain 1, Addressing caregiver mental health, captured themes of overwhelm and invisibility, diverse caregiving roles, and the need for time and resources to support wellbeing conversations. Domain 2, Housing the mental health conversation, encompassed themes of screening preferences, caregiver agency in confidentiality, delivery of feedback, and access to tailored supports. Conclusions Caregivers and clinicians support routine caregiver mental health discussions in paediatric outpatient settings. Caregivers favour screening at diagnosis and key transitions, with clear, and actionable feedback delivered away from the child. Questions about record-keeping warrant further exploration, as do the perspectives of fathers.

This systematic review and meta-analysis aimed to estimate the overall effectiveness of ASD interventions and identify sources of heterogeneity using frequentist and Bayesian approaches. A systematic search of PubMed/MEDLINE, Embase, Web of Science, and Scopus was conducted for studies published between January 1, 2004, and April 30, 2025. Mainly, randomized controlled trial studies with extractable intervention outcomes were included. A total of 41 studies (n=3,008) were synthesized using random-effects models (REML), Bayesian hierarchical modeling, meta-regression, and sensitivity analyses following PRISMA guidelines. The pooled random-effects estimate showed a significant positive effect of ASD interventions (effect size = 0.506, 95% CI: 0.392-0.619; z = 8.72, p < .001), corresponding to an estimated success proportion of 62% (95% CI: 59%-65%). Heterogeneity was substantial (Q{square}(40) = 238.78, p < .001; I{superscript 2} = 82.45%; {tau}{superscript 2} = 0.069, 95% CI: 0.028-0.137; {tau} = 0.262), with H{superscript 2} = 5.70 and a wide prediction interval (-0.020 to 1.031), indicating strong between-study variability. Bayesian meta-analysis confirmed a comparable effect (posterior mean = 0.619, 95% CrI: 0.592-0.646), with {tau} = 0.273 and I{superscript 2} {approx} 82.5%, and MCMC diagnostics showed stable convergence (R-hat {approx} 1.00). Publication bias analyses indicated significant funnel plot asymmetry (Egger-type regression: z = 3.429, p < .001; weighted regression: t = 9.573, p < .001), while rank correlation was non-significant ({tau} = -0.178, p = .103). Trim-and-fill analysis imputed 10 studies, reducing the pooled effect to 0.374 (95% CI: 0.258-0.491; {tau} = 0.338), though the effect remained significant (p < .001). Sensitivity analyses excluding influential studies yielded a stable effect (0.505, 95% CI: 0.401-0.609), with persistent heterogeneity (I{superscript 2} = 75.49%; Q{square}(38) = 190.21, p < .001; {tau}{superscript 2} = 0.043). Subgroup analyses showed highest effects for digital/technology-based interventions (0.672; 67%; I{superscript 2} = 0%), followed by nutritional (0.635; 64%; I{superscript 2} = 73.81%), behavioral (0.630; 63%; I{superscript 2} = 74.78%), and pharmacological (0.627; 63%; I{superscript 2} = 0%) interventions, while physical/occupational therapies showed lower effects (0.523; 52%; I{superscript 2} = 63.35%) and combined interventions showed borderline effects (0.593; 59%; I{superscript 2} = 19.96%); subgroup differences were significant (Q{square}5) = 22.63, p < .001). Regional effects were similar and non-significant (Q{square}(2) = 0.73, p = .694): North America (0.619; I{superscript 2} = 84.21%), Europe (0.626; I{superscript 2} = 62.34%), and Asia (0.659; I{superscript 2} = 0%). Age at intervention onset did not significantly moderate effects (Q{square}(5) = 0.98, p = .964), although variability was observed across children, adolescents, adults, and toddlers. Meta-regression identified significant moderators including intervention context (Q{square}= 18.159, p = .020), outcome domain (Q{square}= 19.588, p = .003), age at start (Q{square}= 17.795, p = .003), and intervention category (Q{square}= 31.714, p < .001), while follow-up duration and intervention duration were not significant. Bayesian subgroup analyses confirmed robustness, with strongest evidence for pharmacological (BF [-&gt;] {infty}), behavioral (BF {approx} 832.50), and digital interventions (BF {approx} 30.92). In conclusion, ASD interventions demonstrate a moderate and statistically significant overall effect ([~]0.50-0.62), with substantial heterogeneity driven primarily by intervention type, context, and participant characteristics, and findings were consistent across frequentist, Bayesian, and sensitivity analyses, supporting robust but context-dependent effectiveness.

Introduction Social media is a central part of young people's lives, yet research on its mental health effects remains mixed. We posit that these inconsistencies stem in part from treating youth as a homogeneous group, obscuring distinct behavioral patterns associated with divergent mental health and wellbeing trajectories. Objectives This study aimed to: (1) explore heterogeneity in social media engagement styles among U.S. youth aged 15-24; and (2) examine how these engagement styles are embedded within a broader system of mental health, wellbeing, emotional regulation, belonging, family and neighborhood context, and stress and adversity. Methods Data were drawn from a 2024 nationally representative cross-sectional survey of 2,563 U.S. youth, conducted as part of the Youth Mental Health Tracker initiative. We employed unsupervised clustering to identify five distinct social media engagement profiles. Subsequently, we used Bayesian network-based causal discovery to examine (a) upstream factors that emerge as drivers of engagement styles and (b) downstream outcomes influenced by profile membership in the learned system. Results Five profiles were identified: the Perpetually Plugged-In (31.3%), characterized by near-constant multifaceted social media use, for both positive and negative purposes across multiple domains of life; the Burned-Out Browsers (21.9%), with high exposure to negative and comparison-based content with frequent attempts to disengage; the Practical Navigators (20.7%) who engage in structured, goal-oriented use focused on learning, hobbies, and maintaining connections; the Positive Engagers (13.6%) with high social and identity-driven engagement; and the Light Touch Users (12.5%) who have low overall engagement and limited reliance on social media for connection, identity, or support. Causal analyses revealed that the Perpetually Plugged-In and Burned-Out Browsers had the worst mental health and wellbeing, with their engagement driven by different reasons. While both engagement profiles were influenced by similar psychosocial risk factors, they were distinguished by their dominant drivers: contemporaneous social stressors (bullying, discrimination, and emotional dysregulation) for Perpetually Plugged-In youth, versus adverse childhood experiences for Burned-Out Browsers. In contrast, Positive Engagers reported high social media engagement alongside the highest levels of social wellbeing, using social media for identity exploration and social support within a context of low cumulative stress and adversity. Conclusions Findings suggest that youth social media risk is not driven by intensity of use alone, but by the interaction between engagement style and offline emotional and social conditions. Policies focused solely on restricting access risk overlooking these differences and may inadvertently sever important sources of connection for many youth. Strategies should identify experiential risk signals while strengthening supportive contexts that enable healthier engagement. Overall, youth social media use is best understood as part of a broader psychosocial system, and recognizing this heterogeneity is essential for designing more targeted, equitable, and evidence-based interventions.

Previous research links Adverse Childhood Experiences (ACEs) with problem gambling, but most studies rely on retrospective reporting and focus narrowly on maltreatment, overlooking adversities such as parental mental health issues. Using data on 3794 young adults in the Avon Longitudinal Study of Parents and Children, we examined longitudinal associations between 10 prospectively measured ACEs (individually and cumulatively), and moderate-risk/problem gambling (Problem Gambling Severity Index >=3) at ages 17, 20 and 24, adjusted for socioeconomic and other background factors. Population attributable fractions (PAFs) estimated proportions of cases potentially attributable to ACEs. Most ACEs were associated with higher odds of moderate-risk/problem gambling across ages (24/30 estimates) after adjustment, though effect sizes were generally small (median adjusted odds ratio [aOR] 1.31, interquartile range 1.24-1.59), and confidence intervals (CIs) wide. Sexual abuse showed the strongest association (aORs 2.4-4.2, CIs 0.5-10.5), while bullying and parental conviction were associated at ages 17 and 20 only, parental separation age 24 only. Evidence for a dose-response relationship was weak. PAFs suggested ACEs accounted for up to 12% of moderate-risk/problem gambling cases. These findings highlight potential impacts of ACEs on later gambling behaviour, but imprecise estimates suggest findings should be interpreted cautiously and strengthened through larger datasets and meta-analyses.