Search Results (1,576)

This study explores the potential of immersive virtual reality (IVR) to enhance knowledge transfer in vocational education, particularly in bridging the gap between academic learning and practical workplace application. The focus lies on relevant predictors for actual learning transfer, namely knowledge acquisition and the transfer-related self-efficacy. Additionally, the Cognitive Affective Model of Immersive Learning (CAMIL) is used to investigate potential predictors in IVR learning. This approach allows for empirical testing of the CAMIL and validation of its assumptions using empirical data. To address the research questions, a quasi-experimental field study was conducted with 141 retail trainees at a German vocational school. Participants were assigned to either an IVR group or a control group receiving traditional instruction. The intervention spanned four teaching sessions of 90 min each, focusing on the design of a retail sales area based on sales-promoting principles. To assess subject-related learning outcomes, a domain-specific knowledge test was developed. In addition, transfer-related self-efficacy and other relevant constructs were measured using Likert-scale questionnaires. The results show that IVR-based instruction significantly improves knowledge acquisition and transfer-related self-efficacy compared to traditional teaching methods. In terms of the CAMIL-based mechanisms, significant correlations were found between transfer-related self-efficacy and factors such as interest, motivation, academic self-efficacy, embodiment, and self-regulation. Additionally, correlations were found between knowledge acquisition and relevant predictors such as interest, motivation, and self-regulation. These findings underscore IVR’s potential to facilitate knowledge transfer in vocational school, highlighting the need for further research on its long-term effects and the actual application of learned skills in real-world settings. Full article

Muscle–joint flexibility is defined as the ability of a muscle to stretch in a controlled manner, allowing a wide range of movement at the joints. While numerous methodologies exist for improving flexibility, few studies have investigated the role of athletes’ perceptual processes and awareness related to their own body and movement control during such training. In this pilot study, we explored how two different training protocols—static and dynamic stretching (control group, CON) and multi-joint resistance training (experimental group, EXP)—influence both flexibility and psychophysical awareness, understood as a multidimensional construct involving perceived flexibility improvements, self-assessed control over exercise execution, and cognitive-emotional responses such as engagement, motivation, and satisfaction during physical effort. The study involved 24 male amateur track-and-field athletes (mean age 23 ± 2.5 years), randomized into two equal groups. Over 12 weeks, both groups trained three times per week. Flexibility was assessed using the Sit and Reach Test at three time points (pre-, mid-, and post-intervention). A 2 × 3 mixed ANOVA revealed a significant group × time interaction (F = 20.17, p < 0.001), with the EXP group showing greater improvements than the CON group. In the EXP group, Sit and Reach scores increased from pre = 28.55 cm (SD = 4.91) to mid = 29.39 cm (SD = 4.67) and post = 29.48 cm (SD = 4.91), with a significant difference between pre and post (p = 0.01; d = 0.35). The CON group showed minimal changes, with scores of pre = 28.66 cm (SD = 4.92), mid = 28.76 cm (SD = 5.03), and post = 28.84 cm (SD = 5.10), and no significant difference between pre and post (p = 0.20; d = 0.04). Psychophysical awareness was assessed using a custom questionnaire structured on a 5-point Likert scale, with items addressing perception of flexibility, motor control, and exercise-related bodily sensations. The questionnaire showed excellent internal consistency (Cronbach’s α = 0.92). Within the EXP group, psychophysical awareness increased significantly (from 3.50 to 4.17; p = 0.01; d = 0.38), while no significant change occurred in the CON group (p = 0.16). Post-hoc power analysis confirmed small to moderate effect sizes within the EXP group, although between-group differences lacked sufficient statistical power. These results suggest that resistance training may improve flexibility and concurrently enhance athletes’ psychophysical self-awareness more effectively than traditional stretching. Such findings offer practical implications for coaches seeking to optimize flexibility training by integrating alternative methods that promote both physical and perceptual adaptations. Full article

Objectives: The objectives are to evaluate the influence of different maternal characteristics on ultrasound image quality and operator satisfaction, and to assess, preliminarily, a rating scale to stratify the difficulty level of ultrasound examination in early gestation. Methods: A multicentric observational study of ultrasound scans was carried out on singleton pregnant women undergoing routine gestational ultrasound at 11–14 weeks and 19–21 weeks of gestation at two Prenatal Care Centers in the Apulia region (Southern Italy). Inclusion criteria included the presence of one or more limiting features, i.e., obesity, retroverted uterus, myomas, previous abdominal surgery, and limited echo-absorption. Each woman was given an overall pre-echographic limiting score from 0 to 9. The outcome measure was the operator’s satisfaction with the examination, scored on a Likert scale. Nested linear regression models (raw, semi- and fully adjusted) were built for each of the two trimesters on the pre-ecographic limiting score (0–9 points) as dependent variables, with the operator’s satisfaction as the regressor. Results: The whole sample included 445 pregnant women. The two-center samples did not show statistically different baseline features. The operator’s satisfaction with the sonographic examination was significantly (and inversely) related to the pre-echographic limiting score, regardless of the mother’s age, the operator performing the ultrasound, the Hospital Center where the ultrasound examination was performed, and the duration of the sonographic examination. Conclusions: A number of maternal conditions need to be monitored for good ultrasound performance; using a specific rating scale to stratify the level of difficulty of the ultrasound examination at early gestation could represent a potentially useful tool, although it requires further validation. Full article

Objectives: To identify clinical, functional, laboratory, and patient-reported parameters associated with medium-term risk of hospitalization or death among older adults attending a multidisciplinary outpatient clinic, and to assess the predictive performance of these measures for individual risk stratification. Methods: In this cohort study, 350 adults aged ≥65 years were assessed at baseline and followed for an average of 8 months. The primary outcome was a composite of hospitalization or all-cause mortality. Parameters assessed included frailty and comorbidity measures, functional parameters, such as gait speed and grip strength, laboratory biomarkers, and patient-reported measures, such as quality of life (QoL, assessed on a Likert scale) and the presence of depressive symptoms. Predictive performance was evaluated using univariable logistic regression and multivariable modeling. Discriminative ability was assessed via area under the ROC curve (AUC), and selected models were internally validated using repeated k-fold cross-validation. Results: Overall, 40 participants (11.4%) experienced hospitalization or death. Traditional clinical risk indicators, including frailty and comorbidity scores, were significantly associated with the outcome. Patient-reported QoL (AUC = 0.74) and Geriatric Depression Scale (GDS) scores (AUC = 0.67) demonstrated useful overall discriminatory ability, with high specificities at optimal cut-offs, suggesting they could act as “red flags” for adverse outcomes. However, the limited sensitivities of individual predictors underscore the need for more comprehensive screening instruments with improved ability to identify at-risk individuals earlier. A multivariable model that incorporated several predictors did not outperform QoL alone (AUC = 0.79), with cross-validation confirming comparable discriminative performance. Conclusions: Patient-reported measures—particularly quality of life and depressive symptoms—are valuable predictors of hospitalization or death and may enhance traditional frailty and comorbidity assessments in outpatient geriatric care. Future work should focus on developing or integrating screening tools with greater sensitivity to optimize early risk detection and guide preventive interventions. Full article

At the Michael G. DeGroote School of Medicine, a significant component of the MD curriculum involves written narrative reflections on topics related to professional identity in medicine, with written feedback provided by their in-person longitudinal facilitators (LFs). However, it remains to be understood how generative artificial intelligence chatbots, such as ChatGPT (GPT-4), augment the feedback process and how MD students perceive feedback provided by ChatGPT versus the feedback provided by their LFs. In this study, 15 MD students provided their written narrative reflections along with the feedback they received from their LFs. Their reflections were input into ChatGPT (GPT-4) to generate instantaneous personalized feedback. MD students rated both modalities of feedback using a Likert-scale survey, in addition to providing open-ended textual responses. Quantitative analysis involved mean comparisons and t-tests, while qualitative responses were coded for themes and representational quotations. The results showed that while the LF-provided feedback was rated slightly higher in six out of eight survey items, these differences were not statistically significant. In contrast, ChatGPT scored significantly higher in helping to identify strengths and areas for improvement, as well as in providing actionable steps for improvement. Criticisms of ChatGPT included a discernible “AI tone” and paraphrasing or misuse of quotations from the reflections. In addition, MD students valued LF feedback for being more personal and reflective of the real, in-person relationships formed with LFs. Overall, findings suggest that although skepticism regarding ChatGPT’s feedback exists amongst MD students, it represents a viable avenue for deepening reflective practice and easing some of the burden on LFs. Full article

Background/Objectives: Atrial fibrillation (AF) is a very common arrhythmia and the main cause of embolic events. Early diagnosis and treatment are crucial to prevent thromboembolic events. Although DOACs are an important advance in AF management, optimization is required. This study aims to evaluate the newly available evidence and experts’ opinions on the clinical care of AF patients and to develop a set of practical recommendations to improve the management of patients with AF. Methods: A questionnaire was developed on the topics of AF diagnosis, stroke prevention, rate and rhythm control, and management of comorbidities, based on the scientific committee’s judgment and a rapid literature review. The level of agreement of the panelists with each statement was evaluated using the Likert 5-point scale. The results of the questionnaire were discussed in a final meeting and practical recommendations were made. Results: Thirty-five Spanish panelists, all experts in AF management, answered the questionnaire. Most of the statements (78%) reached the levels of agreement or unanimity. Discrepancy (9%) and rejection (13%) were also reported. Conclusions: This study underscores the importance of a 12-lead electrocardiogram to diagnose AF, with wearable devices serving as useful tools; catheter ablation as a superior strategy for restoring and maintaining sinus rhythm compared to pharmacotherapy; the importance of comorbidity management to reduce incidence and recurrence of AF; adherence and persistence as critical factors for the efficacy and safety of anticoagulation; and the preference for DOACs, particularly apixaban and edoxaban, for stroke prevention in patients ≥75 years old or with chronic kidney disease. Full article

Background/Objectives: Otolaryngologists are increasingly using head and neck ultrasound (HNUS). Determining whether a practitioner of HNUS has achieved adequate theoretical knowledge remains a challenge. This study assesses the performance of two large language models (LLMs) in generating multiple-choice questions (MCQs) for head and neck ultrasound, compared with MCQs generated by physicians. Methods: Physicians and LLMs (ChatGPT, GPT4o, and Google Gemini, Gemini Advanced) created a total of 90 MCQs that covered the topics of lymph nodes, thyroid, and salivary glands. Experts in HNUS additionally evaluated all physician-drafted MCQs using a Delphi-like process. The MCQs were assessed by an international panel of experts in HNUS, who were blinded to the source of the questions. Using a Likert scale, the evaluation was based on an overall assessment including six assessment criteria: clarity, relevance, suitability, quality of distractors, adequate rationale of the answer, and an assessment of the level of difficulty. Results: Four experts in the clinical field of HNUS assessed the 90 MCQs. No significant differences were observed between the two LLMs. Physician-drafted questions (n = 30) had significant differences with Google Gemini in terms of relevance, suitability, and adequate rationale of the answer, but only significant differences in terms of suitability compared with ChatGPT. Compared to MCQ items (n = 16) validated by medical experts, LLM-constructed MCQ items scored significantly lower across all criteria. The difficulty level of the MCQs was the same. Conclusions: Our study demonstrates that both LLMs could be used to generate MCQ items with a quality comparable to drafts from physicians. However, the quality of LLM-generated MCQ items was still significantly lower than MCQs validated by ultrasound experts. LLMs are therefore cost-effective to generate a quick draft for MCQ items that afterward should be validated by experts before being used for assessment purposes. In this way, the value of LLM is not the elimination of humans, but rather vastly superior time management. Full article

Background/Objectives: The increased accessibility of information has resulted in a rise in patients trying to self-diagnose and opting for self-medication, either as a primary treatment or as a supplement to medical care. Our objective was to evaluate the reliability, comprehensibility, and readability of the responses provided by ChatGPT 4.0 when queried about the most prevalent orthopaedic problems, thus ascertaining the occurrence of misguidance and the necessity for an audit of the disseminated information. Methods: ChatGPT 4.0 was presented with 26 open-ended questions. The responses were evaluated by two observers using a Likert scale in the categories of diagnosis, recommendation, and referral. The scores from the responses were subjected to subgroup analysis according to the area of interest (AoI) and anatomical region. The readability and comprehensibility of the chatbot’s responses were analyzed using the Flesch–Kincaid Reading Ease Score (FRES) and Flesch–Kincaid Grade Level (FKGL). Results: The majority of the responses were rated as either ‘adequate’ or ‘excellent’. However, in the diagnosis category, a significant difference was found in the evaluation made according to the AoI (p = 0.007), which is attributed to trauma-related questions. No significant difference was identified in any other category. The mean FKGL score was 7.8 ± 1.267, and the mean FRES was 52.68 ± 8.6. The average estimated reading level required to understand the text was considered as “high school”. Conclusions: ChatGPT 4.0 facilitates the self-diagnosis and self-treatment tendencies of patients with musculoskeletal disorders. However, it is imperative for patients to have a robust understanding of the limitations of chatbot-generated advice, particularly in trauma-related conditions. Full article

Objective: This cross-sectional content analysis aimed to investigate how oil pulling is portrayed on YouTube Shorts, focusing on the types of speakers, claims made, and alignment with scientific evidence. The study further explored how the content may influence viewer perception, health behaviors, and the potential spread of misinformation. Methods: On 28 January 2025, a systematic search of YouTube Shorts was performed using the term “oil pulling” in incognito mode to reduce algorithmic bias. English language videos with at least 1000 views were included through purposive sampling. A total of 47 Shorts met the inclusion criteria. Data were extracted using a structured coding framework that recorded speaker type (e.g., dentist, hygienist, influencer), engagement metrics, stated benefits, oil type and regimen, the use of disclaimers or citations, and stance toward oil pulling rated on a 5-point Likert scale. Speaker background and nationality were determined through publicly available channel descriptions or linked websites, with user identities anonymized and ethical approval deemed unnecessary due to the use of publicly available content. In total, 47 videos met the inclusion criteria. Results: Of the 47 YouTube Shorts that met the inclusion criteria, most were posted by influencers rather than dental professionals. These videos predominantly encouraged oil pulling, often recommending coconut oil for 10–15 min daily and citing benefits such as reduced halitosis and improved gum health. However, a smaller subset advanced more extreme claims, including reversing cavities and remineralizing enamel. Notably, US-licensed dentists and dental hygienists tended to discourage or express skepticism toward oil pulling, assigning lower Likert scores (1 or 2) to influencers and alternative health practitioners (often 4 or 5). Conclusions: YouTube Shorts largely promote oil pulling through anecdotal and testimonial-driven content, often diverging from evidence-based dental recommendations. The findings reveal a disconnect between professional dental guidance and popular social media narratives. While some benefits like halitosis reduction may have limited support, exaggerated or misleading claims may result in improper oral hygiene practices. Greater engagement from dental professionals and improved health communication strategies are needed to counteract misinformation and reinforce oil pulling’s role, if any, as an adjunct—not a replacement—for standard oral care. Future studies should explore viewer interpretation, behavioral influence, and cross-platform content patterns to better understand the impact of short-form health videos. Full article

Background: Large Language Models (LLMs) such as ChatGPT, Claude, and Gemini are being increasingly adopted in medicine; however, their reliability in cardiology remains underexplored. Purpose of the study: To compare the performance of three general-purpose LLMs in response to cardiology-related clinical queries. Study design: Seventy clinical prompts stratified by diagnostic phase (pre or post) and user profile (patient or physician) were submitted to ChatGPT, Claude, and Gemini. Three expert cardiologists, who were blinded to the model’s identity, rated each response on scientific accuracy, completeness, clarity, and coherence using a 5-point Likert scale. Statistical analysis included Kruskal–Wallis tests, Dunn’s post hoc comparisons, Kendall’s W, weighted kappa, and sensitivity analyses. Results: ChatGPT outperformed both Claude and Gemini across all criteria (mean scores: 3.7–4.2 vs. 3.4–4.0 and 2.9–3.7, respectively; p < 0.001). The inter-rater agreement was substantial (Kendall’s W: 0.61–0.71). Pre-diagnostic and patient-framed prompts received higher scores than post-diagnostic and physician-framed ones. Results remained robust across sensitivity analyses. Conclusions: Among the evaluated LLMs, ChatGPT demonstrated superior performance in generating clinically relevant cardiology responses. However, none of the models achieved maximal ratings, and the performance varied by context. These findings highlight the need for domain-specific fine-tuning and human oversight to ensure a safe clinical deployment. Full article

This paper examines how artificial intelligence (AI) can be strategically deployed to enhance urban planning and environmental livability in Riyadh by generating data-driven, people-centric design interventions. Unlike previous studies that concentrate primarily on visualization, this research proposes an integrative appraisal framework that combines AI-generated design with site-specific environmental data and native vegetation typologies. This study was conducted across key jurisdictional areas including the Northern Ring Road, King Abdullah Road, Al Rabwa, Al-Malaz, Al-Suwaidi, Al-Batha, and King Fahd Road. Using AI tools, urban scenarios were developed to incorporate expanded pedestrian pathways (up to 3.5 m), dedicated bicycle lanes (up to 3.0 m), and ecologically adaptive green buffer zones featuring native drought-resistant species such as Date Palm, Acacia, and Sidr. The quantitative analysis of post-intervention outcomes revealed surface temperature reductions of 3.2–4.5 °C and significant improvements in urban esthetics, walkability, and perceived safety—measured on a five-point Likert scale with 80–100% increases in user satisfaction. Species selection was validated for ecological adaptability, minimal maintenance needs, and compatibility with Riyadh’s sandy soils. This study directly supports the Kingdom of Saudi Arabia’s Vision 2030 by demonstrating how emerging technologies like AI can drive smart, sustainable urban transformation. It aligns with Vision 2030’s urban development goals under the Quality-of-Life Program and environmental sustainability pillar, promoting healthier, more connected cities with elevated livability standards. The research not only delivers practical design recommendations for planners seeking to embed sustainability and digital innovation in Saudi urbanism but also addresses real-world constraints such as budgetary limitations and infrastructure integration. Full article

Background/Objectives: Mobile healthcare units (MHUs) comprise flexible, ambulatory healthcare teams that deliver community care services, particularly in underserved or remote areas. In Greece, MHUs were pivotal in epidemiological surveillance during the COVID-19 pandemic and are now evolving into a sustainable and integrated service for much-needed community-based healthcare. To support this expanded role, targeted, competency-based training is essential; however, this can pose challenges, especially in coordinating synchronous learning across geographically dispersed teams and in ensuring engagement using an online format. Methods: A nationwide, online training program was developed to improve the knowledge of the personnel members of the Hellenic National Public Health Organization’s MHUs. This program was structured focusing on four core themes: (i) prevention–health promotion; (ii) provision of care; (iii) social welfare and solidarity initiatives; and (iv) digital health skill enhancement. The program was implemented by the University of Crete’s Center for Training and Lifelong Learning from 16 January to 24 February 2025. A multidisciplinary team of 64 experts delivered 250 h of live and on-demand educational content, including health screenings, vaccination protocols, biomarker monitoring, chronic disease management, treatment adherence, organ donation awareness, counseling on social violence, and eHealth applications. Knowledge acquisition was assessed through a pre- and post-training multiple-choice test related to the core themes. Trainees’ and trainers’ qualitative feedback was evaluated using a 0–10 numerical rating scale (Likert-type). Results: A total of 873 MHU members participated in the study, including both healthcare professionals and administrative staff. The attendance rate was consistently above 90% on a daily basis. The average assessment score increased from 52.8% (pre-training) to 69.8% (post-training), indicating 17% knowledge acquisition. The paired t-test analysis demonstrated that this improvement was statistically significant (t = −8.52, p < 0.001), confirming the program’s effectiveness in enhancing knowledge. As part of the evaluation of qualitative feedback, the program was positively evaluated, with 75–80% of trainees rating key components such as content, structure, and trainer effectiveness as “Very Good” or “Excellent.” In addition, using a 0–10 scale, trainers rated the program relative to organization (9.4/10), content (8.8), and trainee engagement (8.9), confirming the program’s strength and scalability in primary care education. Conclusions: This initiative highlights the effectiveness of a structured, online training program in enhancing MHU knowledge, ensuring standardized, high-quality education that supports current primary healthcare needs. Future studies evaluating whether the increase in knowledge acquisition may also result in an improvement in the personnel’s competencies, and clinical practice will further contribute to assessing whether additional training programs may be helpful. Full article

Technology-based strategies for the prevention and management of suicidal behavior are widely referenced for identifying vulnerable groups and for supporting clinical reasoning, decision-making, and appropriate referrals. In this study, we estimated the interface and content validity evidence of an interactive mobile application developed for managing suicidal behavior. This methodological study employed psychometric parameters to evaluate the content and interface of the mobile application, following five action phases: analysis, design, development, implementation, and evaluation. A total of 27 healthcare professionals participated, selected by convenience sampling, all working within the Psychosocial Care Network across different regions of Brazil. Data were collected using an electronic form, the Delphi technique for evaluation rounds, and a Likert scale to achieve consensus. The validity analysis was based on a Content Validity Index (CVI) equal to or greater than 0.80. The results showed that GerenciaVida, a technology developed for healthcare workers—regardless of their level of care or professional category—can be used to screen for suicide risk in the general population and indicate preventive alternatives. The app demonstrated satisfactory indicators of content validity (0.974) and interface validity (0.963), reflecting clarity (0.925), objectivity (1.00), adequacy (0.925), coherence (0.962), accuracy (0.962), and clinical relevance (1.00). The development path of this mobile application provided scientific, technological, and operational support, establishing it as an innovative care tool. It consolidates valid evidence that supports the identification, risk classification, and prevention of suicidal behavior in various healthcare contexts. Full article

Job satisfaction in sub-Saharan Africa is crucial as it directly impacts employee productivity, retention, and overall economic growth, fostering a motivated workforce that drives regional development. In sub–Saharan Africa, poor remuneration, limited professional development opportunities, and inadequate working conditions impact satisfaction. This study examined job-satisfaction predictors among health workers in primary healthcare settings across eight countries: Ethiopia, Kenya, Malawi, Senegal, South Sudan, Tanzania, Uganda, and Zambia. A cross-sectional study surveyed 1711 health workers, assessing five dimensions: employer–2employee relationships, remuneration and recognition, professional development, physical work environment, and supportive supervision. The study was conducted from October 2023 to March 2024. The job-satisfaction assessment tool was adopted from a validated tool originally developed for use in low-income healthcare settings. The tool was reviewed by staff from all the country offices to ensure contextual relevance and organization alignment. The responses were measured on a five-point Likert scale: 0: Not applicable, 1: Very dissatisfied, 2: Dissatisfied, 3: Neutral, 4: Satisfied, and 5: Very satisfied. The analysis employed descriptive and multivariable regression methods. Job satisfaction varied significantly by country. Satisfaction with the employer–employee relationship was highest in Zambia (80%) and lowest in Tanzania (16%). Remuneration satisfaction was highest in Senegal (63%) and Zambia (49%), while it was very low in Malawi (9.8%) and Ethiopia (2.3%). Overall, 44% of respondents were satisfied with their professional development, with Uganda leading (62%) and Ethiopia having the lowest satisfaction level (29%). Satisfaction with the physical environment was at 27%, with Uganda at 40% and Kenya at 12%. Satisfaction with supervisory support stood at 62%, with Zambia at 73% and Ethiopia at 30%. Key predictors of job satisfaction included a strong employer–employee relationships (OR = 2.20, p < 0.001), fair remuneration (OR = 1.59, p = 0.002), conducive work environments (OR = 1.71, p < 0.001), and supervisory support (OR = 3.58, p < 0.001. Improving the job satisfaction, retention, and performance of health workers in sub-Saharan Africa requires targeted interventions in employer–employee relationships, fair compensation, supportive supervision, and working conditions. Strategies must be tailored to each country’s unique challenges, as one-size-fits-all solutions may not be effective. Policymakers should prioritize these factors to build a motivated, resilient workforce, with ongoing research and monitoring essential to ensure sustained progress and improved healthcare delivery. Full article

Background: MRA is used in our center for monitoring post-treatment residual aneurysmal neck and stent patency. IV CBCT offers better spatial resolution and may provide significant advantages. Objective: This study investigates the image quality of IV CBCT compared to that of MRA for the follow-up of intracranial aneurysms. Materials and Methods: In this prospective cohort study, 97 patients (mean age: 63.1 ± 11.7; 75 women and 22 men) with 114 treated cerebral aneurysms were included from July 2023 to April 2024. All patients underwent IV CBCT and MRA on the same day. Two neurointerventional radiologists assessed image quality using a five-point Likert scale on two separate occasions six weeks apart. Diagnostic values were evaluated across six parameters. Intra-observer and inter-observer agreements were calculated. Subgroup analyses were performed. Results: Overall, IV CBCT and MRA are comparable in terms of their ability to assess parent vessel status and the degree of artifacts (p > 0.05) though MRA shows a slight advantage in evaluating residual aneurysmal neck (p = 0.05). For clipped aneurysms, IV CBCT is superior in assessing residual aneurysmal neck (OR = 16.0, p < 0.001) and parent vessel status (OR = 15.1, p < 0.001) with significantly fewer artifacts (OR > 100, p < 0.001). For aneurysms solely treated with stents, IV CBCT is superior in assessing residual aneurysmal neck (OR > 20, p = 0.002) and parent vessel status (OR > 20, p = 0.002) with significantly fewer artifacts (OR > 20, p = 0.002). IV CBCT outperforms MRA in evaluating stent struts and the vessel wall status of a stented segment when MRA is non-diagnostic. Conclusions: IV CBCT and MRA have their own strengths and roles in the follow-up of post-treatment intracranial aneurysms. Overall, IV CBCT is superior in terms of its assessment of intracranial aneurysms treated solely with stents or surgical clips. Full article