Tag: pubmed

BMC Med Educ. 2025 Jan 6;25(1):23. doi: 10.1186/s12909-024-06362-6.

ABSTRACT

BACKGROUND: Health literacy (HL) is crucial for making informed health decisions. Over one-third of US adults have limited HL, leading to adverse health outcomes. Despite its importance, HL education lacks standardization in medical training. This study evaluates medical learners’ confidence and experiences with HL at the University of South Carolina School of Medicine Greenville’s (USCSOMG) and the Family Medicine Residency Program Greenville (FMRGVL) to propose recommendations for HL instruction.

METHODS: A convergent parallel mixed methods design was used to assess the learners’ experiences with HL training through a student survey and faculty interviews. The study utilized thematic analysis for qualitative data and statistical analysis for quantitative data, focusing on prior and current HL training, confidence in HL application, and perceptions of HL education.

RESULTS: The curriculum at USCSOMG and FMRGVL incorporate active learning strategies, emphasizing HL and patient communication. Most participants reported high confidence in their HL knowledge and skills. The preferred teaching methods were hands-on clinical interactions, observing clinical interactions, and interactive lessons. Barriers to using HL interventions included time constraints and lack of real-world experience. Faculty recommended time prioritization and collaborative strategies to overcome these barriers.

CONCLUSIONS: This study highlights the impact of curricular approaches at USCSOMG and FMLGVL on learners’ confidence in engaging with patients facing low health literacy (LHL). To overcome barriers like time constraints and real-world challenges, medical educators should consider implementing competency-based exams, increasing practical opportunities for health literacy skills, and incorporating continuous curriculum evaluation based on faculty and student feedback. HL training and evaluation are essential to ensure that medical learners are adequately prepared to meet diverse patient literacy needs.

CLINICAL TRIAL NUMBER: Not Appliable.

PMID:39762861 | DOI:10.1186/s12909-024-06362-6

Int J Med Inform. 2024 Dec 31;195:105782. doi: 10.1016/j.ijmedinf.2024.105782. Online ahead of print.

ABSTRACT

BACKGROUND: Solid organ transplantation (SOT) is vital for end-stage organ failure but faces challenges like organ shortage and rejection. Artificial intelligence (AI) offers potential to improve outcomes through better matching, success prediction, and automation. However, the evolution of AI in SOT research remains underexplored. This study uses bibliometric analysis to identify trends, hotspots, and key contributors in the field.

METHODS: 821 articles from the Web of Science Core Collection were exported for analysis. Microsoft Excel 2021 was used for descriptive statistics. VOSviewer, CiteSpace, Scimago Graphica, and Biblioshiny were used for bibliometric analysis. The ggalluvial package in R was utilized to create Sankey diagrams, and top articles were selected based on citation count.

RESULTS: This analysis reveals the rapid expansion of AI in SOT. Key areas include robotic surgery, organ allocation, outcome prediction, immunosuppression management, and precision medicine. Robotic surgery has improved transplant outcomes. AI algorithms optimize organ matching and enhance fairness. Machine learning models predict outcomes and guide treatment, while AI-based systems advance personalized immunosuppression. AI in precision medicine, including diagnostics and imaging, is crucial for transplant success.

CONCLUSION: This study highlights AI’s transformative potential in SOT, with significant contributions from countries like the USA, Canada, and the UK. Key institutions such as the University of Toronto and the University of Pittsburgh have played vital roles. However, practical challenges like ethical issues, bias, and data integration remain. Fostering international and interdisciplinary collaborations is crucial for overcoming these challenges and accelerating AI’s integration into clinical practice, ultimately improving patient outcomes.

PMID:39761617 | DOI:10.1016/j.ijmedinf.2024.105782

Int J Pediatr Otorhinolaryngol. 2025 Jan 2;189:112214. doi: 10.1016/j.ijporl.2025.112214. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aims to evaluate musical pitch and timbre perception in children who stutter and compare the results with typically developing children.

METHODS: A total of 50 participants were included in the study, consisting of 25 children with stuttering (mean age = 10.06 years; range 6-17 years) and 25 typically developing children (mean age = 10.38 years; range 7-16 years). Participants were administered Pitch Direction Discrimination (PDD) and Timbre Recognition (TR) tests in the original form of The Clinical Assessment of Music Perception. Both subtests were administered in a quiet room, and the children used headphones to receive auditory stimuli.

RESULTS: The mean PDD score of the stuttering group was 3.60 semitones (SD = 2.71), while the mean score of the typically developing children was 2.26 semitones (SD = 1.43). In the TR test, the mean accuracy of the stuttering group was 53.17 % (SD = 21.69), while the mean accuracy of the non-stuttering group was 65.33 % (SD = 19.64). The difference between the two groups was statistically significant in the PDD (t(48) = 2.17, p = 0.03) and TR (t(48) = -2.08, p = 0.04) tests.

CONCLUSIONS: The study found that children who stuttered had poorer pitch and timbre musical perception skills than age-matched peers who were typically developing children. The lower success rates of the stuttering group on both tests may indicate general deficits in auditory processing, which could be related to attention and short-term memory processing.

PMID:39761608 | DOI:10.1016/j.ijporl.2025.112214

Pediatr Emerg Care. 2025 Jan 7. doi: 10.1097/PEC.0000000000003315. Online ahead of print.

ABSTRACT

OBJECTIVE: Evaluate the accuracy and reliability of various generative artificial intelligence (AI) models (ChatGPT-3.5, ChatGPT-4.0, T5, Llama-2, Mistral-Large, and Claude-3 Opus) in predicting Emergency Severity Index (ESI) levels for pediatric emergency department patients and assess the impact of medically oriented fine-tuning.

METHODS: Seventy pediatric clinical vignettes from the ESI Handbook version 4 were used as the gold standard. Each AI model predicted the ESI level for each vignette. Performance metrics, including sensitivity, specificity, and F1 score, were calculated. Reliability was assessed by repeating the tests and measuring the interrater reliability using Fleiss kappa. Paired t tests were used to compare the models before and after fine-tuning.

RESULTS: Claude-3 Opus achieved the highest performance amongst the untrained models with a sensitivity of 80.6% (95% confidence interval [CI]: 63.6-90.7), specificity of 91.3% (95% CI: 83.8-99), and an F1 score of 73.9% (95% CI: 58.9-90.7). After fine-tuning, the GPT-4.0 model showed statistically significant improvement with a sensitivity of 77.1% (95% CI: 60.1-86.5), specificity of 92.5% (95% CI: 89.5-97.4), and an F1 score of 74.6% (95% CI: 63.9-83.8, P < 0.04). Reliability analysis revealed high agreement for Claude-3 Opus (Fleiss κ: 0.85), followed by Mistral-Large (Fleiss κ: 0.79) and trained GPT-4.0 (Fleiss κ: 0.67). Training improved the reliability of GPT models (P < 0.001).

CONCLUSIONS: Generative AI models demonstrate promising accuracy in predicting pediatric ESI levels, with fine-tuning significantly enhancing their performance and reliability. These findings suggest that AI could serve as a valuable tool in pediatric triage.

PMID:39761573 | DOI:10.1097/PEC.0000000000003315

Chaos. 2025 Jan 1;35(1):013114. doi: 10.1063/5.0227075.

ABSTRACT

The impact of resource allocation on the dynamics of epidemic spreading is an important topic. In real-life scenarios, individuals usually prioritize their own safety, and this self-protection consciousness will lead to delays in resource allocation. However, there is a lack of systematic research on the impact of resource allocation delay on epidemic spreading. To this end, a coupled model for resource allocation and epidemic spreading is proposed, which considers both the allocation decisions and delay behavior of individuals with limited resources. Through theoretical analysis, the influence mechanism of resource allocation delay on epidemic spreading is deduced, and the relationship among epidemic threshold, delay time, and the fraction of cautious individuals is obtained, and finally, the stability of the solution under different conditions is proven. Furthermore, the dynamic characteristics of epidemic spreading under the influence of the two factors are systematically studied by combining numerical simulation and theoretical analysis. The results show that the impact of delay behavior exhibits nonlinear characteristics, namely, appropriate delay can enhance control effectiveness, while excessive delay results in insufficient resource allocation and consequently increases infection risk. Particularly, an optimal delay that maximizes the epidemic threshold is identified. In addition, an increase in the proportion of cautious individuals can significantly increase the epidemic threshold, but an excessively high proportion can severely constrain resource allocation, which reduces the control effectiveness. The results of this study provide scientific evidence for developing more effective epidemic control strategies, particularly in optimizing resource allocation and improving control outcomes.

PMID:39761558 | DOI:10.1063/5.0227075

J Med Internet Res. 2025 Jan 6;27:e66220. doi: 10.2196/66220.

ABSTRACT

BACKGROUND: The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging.

OBJECTIVE: This paper aims to develop a retrieval-augmented generation (RAG) architecture for medical question answering pertaining to clinicians’ queries on emerging issues associated with health-related topics, using user-generated medical information on social media.

METHODS: We proposed a two-layer RAG framework for query-focused answer generation and evaluated a proof of concept for the framework in the context of query-focused summary generation from social media forums, focusing on emerging drug-related information. Our modular framework generates individual summaries followed by an aggregated summary to answer medical queries from large amounts of user-generated social media data in an efficient manner. We compared the performance of a quantized large language model (Nous-Hermes-2-7B-DPO), deployable in low-resource settings, with GPT-4. For this proof-of-concept study, we used user-generated data from Reddit to answer clinicians’ questions on the use of xylazine and ketamine.

RESULTS: Our framework achieves comparable median scores in terms of relevance, length, hallucination, coverage, and coherence when evaluated using GPT-4 and Nous-Hermes-2-7B-DPO, evaluated for 20 queries with 76 samples. There was no statistically significant difference between GPT-4 and Nous-Hermes-2-7B-DPO for coverage (Mann-Whitney U=733.0; n₁=37; n₂=39; P=.89 two-tailed), coherence (U=670.0; n₁=37; n₂=39; P=.49 two-tailed), relevance (U=662.0; n₁=37; n₂=39; P=.15 two-tailed), length (U=672.0; n₁=37; n₂=39; P=.55 two-tailed), and hallucination (U=859.0; n₁=37; n₂=39; P=.01 two-tailed). A statistically significant difference was noted for the Coleman-Liau Index (U=307.5; n₁=20; n₂=16; P<.001 two-tailed).

CONCLUSIONS: Our RAG framework can effectively answer medical questions about targeted topics and can be deployed in resource-constrained settings.

PMID:39761554 | DOI:10.2196/66220

J Med Internet Res. 2025 Jan 6;27:e52601. doi: 10.2196/52601.

ABSTRACT

BACKGROUND: In recent years, health care has undergone a rapid and unprecedented digital transformation. In many fields of specialty care, such as rheumatology, this shift is driven by the growing number of patients and limited resources, leading to increased use of digital health technologies (DHTs) to maintain high-quality clinical care. Previous studies examined user acceptance of individual DHTs in rheumatology, such as telemedicine, video consultations, and mHealth. However, it is essential to conduct cross-technology and continuous analyses of user acceptance and DHT use to maximize the benefits for all relevant stakeholders.

OBJECTIVE: This study aimed to explore the current acceptance, use, and preferences regarding DHTs among patients in rheumatology care in Germany.

METHODS: Rheumatology patients from 3 clinics in Germany were surveyed to understand their perspectives on DHTs. The survey included main themes, including acceptance, preferences, COVID-19’s impact, potential, and barriers related to DHTs. The data were analyzed using descriptive statistics and correlation analysis.

RESULTS: Out of 337 participants, 53% (179/337) reported using DHTs. Specific technologies included wearables (72/337, 21%), mHealth apps (71/337, 21%), digital therapeutics (32/337, 9%), electronic prescriptions (30/337, 9%), video consultations (15/337, 4%), and at-home blood self-sampling (3/337, 1%). Nearly two-thirds (220/337, 65%) found DHTs useful, and 69% (233/337) held a generally positive attitude toward DHTs. Attitudes shifted positively during the COVID-19 pandemic for 40% (135/337) of participants. Higher education was more prevalent among DHT users (114/179, 63.7%) compared with nonusers (42/151, 27.8%; P=.02). The main potential benefits identified were location-independent use (244/337, 72%) and time-independent use (216/337, 64%). Key barriers included insufficient user knowledge (165/337, 49%) and limited information on DHTs (134/337, 40%).

CONCLUSIONS: Patient acceptance and use of DHTs in rheumatology is increasing in Germany. A prospective, standardized monitoring of digital transformation in rheumatology care is highly needed.

PMID:39761546 | DOI:10.2196/52601

Retina. 2025 Jan 2. doi: 10.1097/IAE.0000000000004394. Online ahead of print.

ABSTRACT

PURPOSE: To report one-year real-world evidence on intraocular inflammation (IOI) adverse events (AEs) in patients undergoing faricimab therapy in a tertiary care hospital.

METHODS: A retrospective review of electronic medical records was conducted for patients receiving faricimab treatment for neovascular age-related macular degeneration (nAMD) and diabetic macular edema (DME) at Moorfields Eye Hospital between September 1st, 2022, and August 31st, 2023. The primary outcome was the incidence of IOI (excluding endophthalmitis).

RESULTS: 2 318 eyes from 1 860 patients were included and underwent a total of 10 297 injections. A total of 20 eyes (16 patients) had ≥ 1 adverse event of IOI. Estimated incidence of IOI was 0.19% per injection (95%CI 0.12-0.30), 0.86% per eye (95% CI 0.53- 1.33] and 0.86% per patient (95%CI 0.49- 1.39). IOI mostly occurred within the first injections (median 3.5 injections, range 1-10). All cases presented with anterior uveitis and were associated with vitritis in 4 eyes (20%). No cases of posterior uveitis or evidence of retinal vascular occlusion were reported. There was no statistically significant difference between mean visual acuity before and after IOI event (0.40 logMAR and 0.378 logMAR respectively, p = .26).

CONCLUSION: In this real-world report, faricimab was well tolerated with an incidence of IOI-related AEs consistent to that observed in registration trials. The AEs were generally mild and had a favourable prognosis.

PMID:39761510 | DOI:10.1097/IAE.0000000000004394

Eur J Public Health. 2025 Jan 6:ckae220. doi: 10.1093/eurpub/ckae220. Online ahead of print.

ABSTRACT

Maternal mortality remains a critical global health challenge, with 95% of deaths occurring in low-income countries. While progress was made from 2000 to 2015, regions such as Ethiopia continue to experience high maternal mortality rates, impeding the achievement of the sustainable development goal to reduce maternal deaths to 70 per 100 000 live births by 2030. This study evaluated the effectiveness of a Social and Behavior Change Communication (SBCC) intervention to improve maternal health behaviors. A community-randomized trial was conducted in three districts of Jimma Zone, rural Ethiopia, involving 5057 women. Sixteen primary healthcare units were randomly assigned to either the intervention (SBCC) or control (standard care) group. Data on socio-demographics, antenatal care (ANC) visits, maternal health knowledge, attitudes, and institutional childbirth rates were collected at baseline and endline. Statistical analyses included t-tests, effect sizes, and generalized estimating equations. The intervention group demonstrated significant improvements. Maternal health knowledge increased from 5.68 to 7.70 (P < .001, effect size = 0.34), attitudes improved from 37.49 to 39.73 (P < .001, effect size = 0.29), and ANC visits rose from 3.27 to 4.21 (P < .001, effect size = 0.50). Institutional childbirth rates increased from 0.52 to 0.71 (P < .001, effect size = 0.18). ANC attendance (B = 0.082, P = .002) and positive attitudes (B = 0.055, P < .001) were significant predictors of institutional childbirth. The SBCC intervention significantly enhanced maternal health knowledge, attitudes, ANC utilization, and institutional childbirth rates, highlighting the value of community-based strategies in improving maternal health behaviors.

PMID:39761508 | DOI:10.1093/eurpub/ckae220

J Comput Assist Tomogr. 2024 Nov 13. doi: 10.1097/RCT.0000000000001693. Online ahead of print.

ABSTRACT

OBJECTIVES: The aims of the study are to predict lung function impairment in patients with connective tissue disease (CTD)-associated interstitial lung disease (ILD) through computed tomography (CT) quantitative analysis parameters based on CT deep learning model and density threshold method and to assess the severity of the disease in patients with CTD-ILD.

METHODS: We retrospectively collected chest high-resolution CT images and pulmonary function test results from 105 patients with CTD-ILD between January 2021 and December 2023 (patients staged according to the gender-age-physiology [GAP] system), including 46 males and 59 females, with a median age of 64 years. Additionally, we selected 80 healthy controls (HCs) with matched sex and age, who showed no abnormalities in their chest high-resolution CT. Based on our previously developed RDNet analysis model, the proportion of the lung occupied by reticulation, honeycombing, and total interstitial abnormalities in CTD-ILD patients (ILD% = total interstitial abnormal volume/total lung volume) were calculated. Using the Pulmo-3D software with a threshold segmentation method of -260 to -600, the overall interstitial abnormal proportion (AA%) and mean lung density were obtained. The correlations between CT quantitative analysis parameters and pulmonary function indices were evaluated using Spearman or Pearson correlation coefficients. Stepwise multiple linear regression analysis was used to identify the best CT quantitative predictors for different pulmonary function parameters. Independent risk factors for GAP staging were determined using multifactorial logistic regression. The area under the ROC curve (AUC) differentiated between the CTD-ILD groups and HCs, as well as among GAP stages. The Kruskal-Wallis test was used to compare the differences in pulmonary function indices and CT quantitative analysis parameters among CTD-ILD groups.

RESULTS: Among 105 CTD-ILD patients (58 in GAP I, 36 in GAP II, and 11 in GAP III), results indicated that AA% distinguished between CTD-ILD patients and HCs with the highest AUC value of 0.974 (95% confidence interval: 0.955-0.993). With a threshold set at 9.7%, a sensitivity of 98.7% and a specificity of 89.5% were observed. Both honeycombing and ILD% showed statistically significant correlations with pulmonary function parameters, with honeycombing displaying the highest correlation coefficient with Composite Physiologic Index (CPI, r = 0.612). Multiple linear regression results indicated honeycombing was the best predictor for both the Dlco% and the CPI. Furthermore, multivariable logistic regression analysis identified honeycombing as an independent risk factor for GAP staging. Honeycombing differentiated between GAP I and GAP II + III with the highest AUC value of 0.729 (95% confidence interval: 0.634-0.811). With a threshold set at 8.0%, a sensitivity of 79.3% and a specificity of 57.4% were observed. Significant differences in honeycombing and ILD% were also noted among the disease groups (P < 0.05).

CONCLUSIONS: An AA% of 9.7% was the optimal threshold for differentiating CTD-ILD patients from HCs. Honeycombing can preliminarily predict lung function impairment and was an independent risk factor for GAP staging, offering significant clinical guidance for assessing the severity of the patient’s disease.

PMID:39761506 | DOI:10.1097/RCT.0000000000001693