This bibliometric analysis investigates the evolving landscape of artificial intelligence in pediatric nursing care, leveraging bibliometric techniques and visualization to analyze 317 publications indexed in Web of Science (2000-2024). We conducted citation and co-occurrence analyses of keywords, utilizing VOSviewer to map the scientific knowledge base. Results indicate an exponential growth trajectory in publications and citation impact, particularly post-2019, with the United States as the leading contributor. Thematic analysis reveals a distinct focus on symptom management, emotional support, and family-centered care within pediatric artificial intelligence nursing research, diverging from the predominantly disease-centric focus in general medical artificial intelligence literature. Five key thematic clusters emerged: (1) clinical and disease-focused pediatric nursing, (2) technology and innovation in nursing education and practice, (3) pain and psychological well-being in pediatric surgical patients, (4) adolescent mental health and COVID-19's impact, and (5) family-centered care and holistic pediatric nursing. This study underscores the transformative potential of artificial intelligence to augment pediatric nursing practice, enabling personalized and holistic care. These findings provide crucial insights for nursing informatics specialists, researchers, and clinicians to guide future research, address ethical implications, and develop evidence-based implementation strategies for integrating artificial intelligence into pediatric care.

Major depressive disorder (MDD) and rheumatic diseases (RD) interact to exacerbate disease outcomes. The purpose of this study was to assess the prevalence and associated factors of depression in RD patients in order to identify independent predictors of mental health disorders risk and apply cluster analysis to identify homogeneous groups in a population of approximately 47 patients with RD-MDD to achieve precise treatment and early prevention of complications.

In total, 205 RD patients were included in this study. We used the Profile of Mood State (POMS) and Patient Health Questionnaire-9 (PHQ-9) to assess the patients' state of mind. A cluster analysis was applied according to six clinical and serological variables to define different subgroups of patients.

The rate of depression in RD patients in our study was 22.9%. Sex (female), disease duration, and disease activity are risk factors for the development of depression. Albumin is a protective factor for MDD. RD-MDD patients were clustered in two groups. Cluster 1 (n = 30, 63.8%): patients were of older age, lower education and income levels, low disease activity, and mild depressive symptoms. Cluster 2 (n = 17, 36.2%): Young women with higher education and income levels, high disease activity, and more severe depressive symptoms.

Our findings provide evidence indicating that RD-MDD presents varying clinical phenotypes and the treatment varies accordingly, suggesting the need for individualized treatment. Key Points • Depression is often comorbid in patients with rheumatic diseases. The two interact and aggravate the patient's condition. • The rate of depression in RD patients in our study was 22.9%. Sex (female), disease duration, and disease activity are risk factors for the development of depression. Albumin is a protective factor for MDD. • RD-MDD patients were clustered in two groups through cluster analysis in order to guide individualized treatment.

Cannabis use disorder (CUD) is highly prevalent with ∼44 million cases worldwide. CUD has been associated with compulsive use despite experiencing adverse psychosocial outcomes. Such adverse outcomes of CUD have been attributed to altered cognition - a set of mental processes that support the organisation and implementation of goal-directed behaviour. However, the evidence is mixed and limited by methodological issues including inconsistent assessment of CUD and metrics of cannabis use.

This study examined distinct domains of cognition (i.e., executive function, working memory, episodic memory, verbal reasoning, attention, IQ) in 115 participants aged 18.5 to 32.5 years. We compared performance between 83 participants who endorsed a CUD and 32 controls. We also explored whether the level of problematic cannabis use, and cannabis grams/past month was associated with cognition in CUD. All analyses accounted for alcohol/nicotine use and trait anxiety.

CUD compared to control participants showed significantly lower IQ, with a strong effect size (p < .001, d = 0.862), which was driven by lower verbal IQ, and survived adjusting for education years. There were no other significant effects of group or associations between cognition, level of problematic cannabis use, or dosage.

Altered cognition in young adults who endorse a CUD may be specific to verbal IQ. Future work is required to confirm whether these findings generalise to CUD samples across the lifespan, including the most vulnerable individuals with a CUD who are seeking or receiving treatment and that endorse comorbid psychopathologies.

Hydroxytyrosol (HT), an olive-derived phenolic compound, possesses well-known antioxidant and anti-inflammatory properties. While its benefits in healthy individuals and as part of extra virgin olive oil are well studied, its preventive role as a dietary supplement in at-risk populations remains less explored. This study investigates the potential of HT supplementation in preventing aging-related diseases in overweight individuals with prediabetes.

A randomized, double-blind, placebo-controlled trial was conducted in adults with overweight and prediabetes (40-70 years). For 16 weeks, volunteers consumed either 15 mg of HT or a placebo daily. The primary outcome were oxidized LDL (oxLDL) levels, while secondary outcomes included biochemical and metabolic parameters, oxidative stress and inflammation biomarkers, and lifestyle assessments. Compliance was verified through urinary HT-3'-sulphate levels.

A total of 52 participants were recruited and randomized, with 49 completing the study. They were then allocated to either the HT-treated group (n = 24) or the placebo group (n = 25). Compliance was confirmed, as the HT-supplemented group showed increased urinary HT-3'-sulphate levels, whereas the placebo group exhibited a significant decrease (p = 0.039). Compared with placebo, HT supplementation significantly reduced oxLDL levels (p = 0.045), protein carbonyls (p = 0.031), and 8-OHdG (p < 0.01). Additionally, it prevented a decline in total antioxidant status (p < 0.01) and GPx activity (p < 0.01). An anti-inflammatory effect was also observed, with reduced IL-6 levels (p = 0.05). No significant changes were found in lipid profile, anthropometric parameters, or lifestyle factors such as sleep, mental well-being, or physical capacity. No adverse events were observed throughout the intervention.

Chronic supplementation with 15 mg/day of HT for 16 weeks significantly improved antioxidant and anti-inflammatory status in individuals with overweight and prediabetes, suggesting a potential preventive role against aging-related diseases.

NCT06295913 (https://clinicaltrials.gov/study/NCT06295913?intr=Hydroxytyrosol&amp;page=2&amp;rank=1).

Missed injuries (MIs) remain a significant and potentially preventable complication in trauma care, often associated with increased morbidity, mortality, prolonged hospitalization, and legal consequences. Despite decades of recognition, MIs continue to challenge trauma teams, particularly in complex, multi-injury scenarios.

This study aims to review the literature and identify the most relevant factors contributing to missed injuries in trauma patients, highlighting opportunities for prevention and clinical improvement.

A systematic review was conducted according to PRISMA guidelines using PubMed. Inclusion criteria encompassed studies reporting on trauma patients with MIs, their risk factors, prevalence, and clinical outcomes. Exclusion criteria included non-trauma-focused studies, non-peer-reviewed articles, and case reports. Five key domains were assessed: trauma characteristics, injury-specific factors, diagnostic limitations, patient-related challenges, and human (physician) factors.

High Injury Severity Score (ISS), altered mental status (e.g., low Glasgow Coma Scale), polytrauma, and cognitive biases such as anchoring were consistently associated with higher rates of MIs. Non-spinal orthopedic injuries, abdominal and thoracic lesions, and retroperitoneal or diaphragmatic injuries were among the most frequently missed. Diagnostic limitations included false-negative imaging, misinterpretation of radiological exams, and inadequate protocols in unstable patients. Patient factors-such as obesity, advanced age, alcohol or drug intoxication, and pregnancy-also contributed to delayed diagnosis. Inexperience, fatigue, and poor communication were recurrent human factors linked to diagnostic failures. The implementation of Trauma Tertiary Surveys (TTS) significantly reduced MI incidence and improved detection of occult injuries.

Missed injuries are multifactorial events influenced by the complexity of trauma, diagnostic limitations, patient characteristics, and human error. Proactive strategies, including TTS, heightened awareness of injury-specific challenges, improved imaging protocols, and fostering a collaborative trauma culture, are critical to minimizing missed diagnoses and enhancing trauma care quality.

Major depressive disorders significantly impact the lives of individuals, with varied treatment responses necessitating personalized approaches. Shared decision-making (SDM) enhances patient-centered care by involving patients in treatment choices. To date, instruments facilitating SDM in depression treatment are limited, particularly those that incorporate personalized information alongside general patient data and in cocreation with patients.

This study outlines the development of an instrument designed to provide patients with depression and their clinicians with (1) systematic information in a digital report regarding symptoms, medical history, situational factors, and potentially successful treatment strategies and (2) objective treatment information to guide decision-making.

The study was co-led by researchers and patient representatives, ensuring that all decisions regarding the development of the instrument were made collaboratively. Data collection, analyses, and tool development occurred between 2017 and 2021 using a mixed methods approach. Qualitative research provided insight into the needs and preferences of end users. A scoping review summarized the available literature on identified predictors of treatment response. K-means cluster analysis was applied to suggest potentially successful treatment options based on the outcomes of similar patients in the past. These data were integrated into a digital report. Patient advocacy groups developed treatment option grids to provide objective information on evidence-based treatment options.

The Instrument for shared decision-making in depression (I-SHARED) was developed, incorporating individual characteristics and preferences. Qualitative analysis and the scoping review identified 4 categories of predictors of treatment response. The cluster analysis revealed 5 distinct clusters based on symptoms, functioning, and age. The cocreated I-SHARED report combined all findings and was integrated into an existing electronic health record system, ready for piloting, along with the treatment option grids.

The collaboratively developed I-SHARED tool, which facilitates informed and patient-centered treatment decisions, marks a significant advancement in personalized treatment and SDM for patients with major depressive disorders.

Early detection of emerging affective episodes is crucial in managing bipolar disorders (BD). Passive sensing-passive data collection via smartphone or wearable-offers a promising solution by potentially capturing altered activity, communication, and sleep patterns, indicative of manic and depressive episodes. Recently, statistical process control (SPC) has been introduced to psychopathology as a novel approach to identifying out-of-bounds processes. However, its application to mobile sensing data and to BD remains unexplored. To investigate SPC's potential in detecting emerging affective episodes, we utilized the BipoSense study, which monitored patients with BD. The BipoSense data cover 12 months of continuously collected passive sensing data via smartphone app, daily e-diary data, and biweekly expert interviews, that is, 26 in a row, to assess the psychopathological status. Compliance was excellent. A total of 26 depressive and 20 (hypo)manic emerging episodes in 28 patients were included in the analyses. SPC charts and multilevel analyses revealed heterogeneous results. Passive sensing, despite its potential as a low-burden, continuous measurement tool, did not demonstrate robust detection of affective episodes or preepisode weeks. Self-rated current bipolar mood, assessed via e-diary, outperformed passive sensing parameters in predicting current episodes, whereas predicting preepisode weeks was also limited. Notably, SPC with personalized control limits did not surpass established clinical cutoff scores. Even after systematic optimization of SPC settings, the combination of detected emerging episodes in relation to false alarms was insufficient for clinical use. Future studies warrant mobile sensing parameters closer aligned to psychopathology, thereby increasing validity, sensitivity, and specificity. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

The use of dating apps among adolescents is a growing public concern. Past research, which almost exclusively relies on self-reported dating app usage, highlights an increased risk of victimization, but also opportunities to develop personal and social identity, particularly for minoritized youth. Thus, the present study used a mobile sensing app that passively tracked dating app usage over 6 months in 149 adolescents with a wide range of internalizing disorder severity. Thirty-five (23.5%) adolescents used dating apps across the 6 months (indexed by any keyboard input across dating apps), averaging 1.74 (SD = 1.12, range = 1-6) apps per person. At baseline, users (vs. nonusers) were older, more pubertally mature, and more likely to identify as sexual and gender minorities. Controlling for differences in demographic characteristics using propensity score matching, users and nonusers were largely comparable in clinical characteristics, with only a few differences evident: (a) greater self-reported frequency of risky behaviors at baseline and (b) greater number of weeks meeting major depressive disorder criteria across the follow-up period. Exploratory, within-person analyses in a subset of 18 users showed that a greater number of messages sent in dating apps was associated with a greater likelihood of having (subthreshold) depression symptoms in the concurrent week. Importantly, these findings are cross-sectional, and therefore the causal direction of effects remains unclear. Overall, passive monitoring of dating behaviors affords a unique lens on socioemotional development in youth, revealing nuanced relations between dating app usage and mental health among adolescents. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

The child and adolescent trauma screen (CATS) is a widely used tool for assessing posttraumatic stress symptoms in youth; yet very few studies have examined its factor structure, including its measurement invariance and validity, across relevant groups. This information is critical to ensure evidence-based use of the measure while minimizing the risk of inaccurate interpretation.

Utilizing a sample of 259 youth, aged 8-16 years (M = 11.7, SD = 2.4; 63% female), and their caregivers, the factor structure of the CATS was examined, and the optimal factor structure was tested for measurement invariance and construct validity across relevant groups.

A three-factor structure based on International Classification of Diseases 11th Revision criteria for posttraumatic stress disorder that includes "reexperiencing," "avoidance," and "perceived sense of threat" factors based on six items from the total scale was identified as optimal for both CATS self- and caregiver-report (self: χ² = 7.514, root-mean-square error of approximation = .032, comparative fit index = .995, Tucker-Lewis index = .988, standardized root-mean-square residual = .024; caregiver: χ² = 9.663, root-mean-square error of approximation = .049, comparative fit index = .989, Tucker-Lewis index = .971, standardized root-mean-square residual = .032). In addition, measurement invariance was found for this three-factor structure for CATS self-report across youth age, sex, and race. In addition, concurrent validity was found for the CATS self-report total score, as evidenced by significant positive associations with self-reported depression symptoms.

These findings support the use of the total score based on the six-item three-symptom version of the CATS that is based on International Classification of Diseases 11th Revision criteria for posttraumatic stress disorder. Further, these results provide some of the first replicable support for this three-factor structure of the CATS and suggest its use as a highly efficient, short-form screener that may be administered easily across clinical settings. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Multivariate base rates (MBR) of elevations are an emerging psychometric paradigm for enhanced interpretation of multiscale self-report data. The aims of this study were to calculate and compare MBR of scale/subscale elevations on the Personality Assessment Inventory (PAI) and determine the ability of MBR to differentiate between mood disorders (n = 524, k = 3), military-based posttraumatic stress disorder (PTSD; n = 252, k = 2), and coached PTSD-simulator (n = 160, k = 1) groups. Overall, having at least one standard (T ≥ 70) and skyline elevation on clinical scales and clinical subscales was common across the groups. However, differential abnormal elevation thresholds emerged for each group. For instance, it was unusual (i.e., MBR < 10%) for the mood disorders group to have ≥ 1 (9.7%) and for the genuine PTSD group to have ≥ 3 (9.1%) skyline-elevated clinical scales. For subscales, it was unusual for the mood and PTSD groups to have ≥ 3 (7.6%) and ≥ 7 (8.3%) skyline-elevated clinical subscales, respectively. Conversely, PTSD simulators commonly yielded profiles with standard- and skyline elevations on nearly all clinical scales and subscales. MBR cutoffs identified from receiver-operating characteristic curve analyses yielded robust sensitivity (.650-.806) and specificity (.833-.984) in differentiating genuine PTSD and mood disorder groups from PTSD simulators. MBR are useful in differentiating genuine from simulated psychopathology, consistent with broader scale-based infrequency approaches.