Hamza Alduraidi

The 12-lead ECG is central to the evaluation of patients with suspected coronary artery disease (CAD) but has limited diagnostic accuracy for identifying significant obstructive disease prior to elective coronary angiography. We previously developed an artificial intelligence–enhanced ECG (AI-ECG) model for detecting acute coronary occlusion. Whether this model generalizes to identifying angiographically significant CAD in symptomatic patients undergoing elective evaluation remains unknown. In this prospective cohort study, symptomatic outpatients undergoing elective coronary angiography at a tertiary center received standard 12-lead ECGs before the procedure. ECGs were analyzed offline using a previously validated AI-ECG model originally developed for acute coronary occlusion. Patients were categorized as low or intermediate-to-high risk using predefined thresholds. The primary outcome was angiographically significant CAD (≥70% stenosis in a major epicardial vessel or ≥50% stenosis of the left main coronary artery). Among 363 patients (age 58.9 ± 11 years; 55.1% male), 36.1% had angiographically significant CAD. The AI-ECG classified 56% as low risk and 44% as intermediate-to-high risk. After adjustment for conventional clinical characteristics, intermediate-to-high risk remained independently associated with significant CAD (OR 3.12, 95% CI 1.88–5.20; p < 0.001). Model discrimination was good (AUROC 0.79, 95% CI 0.74–0.84), with 60% precision at 80% recall. In summary, the ECG-SMART AI score demonstrated good diagnostic performance for identifying angiographically significant CAD in symptomatic patients undergoing elective coronary angiography. These findings support the generalizability of a previously developed AI-ECG model beyond acute coronary occlusion and suggest that AI-enhanced ECG analysis may complement existing clinical assessment by providing additional noninvasive information to refine pretest probability during elective evaluation.

Background: Alarm fatigue occurs when nursing staff are exposed to clinically excessive false alarms, leading to sensory overload and desensitization. It is a global occupational health concern in intensive care settings, with numerous studies investigating this complex phenomenon. Aim: To investigate the levels of alarm fatigue, its determinants and its association with professional quality of life among nurses in intensive care settings in Amman, Jordan. Study Design: A cross‐sectional descriptive quantitative study was conducted. All nurses were recruited from eight hospitals in Amman, Jordan, representing public, educational and private sectors. Data were collected using alarm fatigue and professional quality of life questionnaires. The data were analysed using chi‐squared, one‐way ANOVA, Kruskal–Wallis and multiple linear regression. Results: In total, 250 nurses were recruited. Overall, 59.2% of nurses reported moderate alarm fatigue, and 28.8% reported severe alarm fatigue. Burnout and secondary traumatic stress were significantly associated with alarm fatigue ( p < 0.001). Five factors (shift type, hospital sector, burnout, secondary traumatic stress and compassion satisfaction) explained 16.5% of the variance in alarm fatigue scores. Compassion satisfaction was not significantly associated with alarm fatigue, suggesting that nurses may retain professional fulfillment despite high alarm exposure. Shift type ( p = 0.031) and hospital sector ( p = 0.042) were significantly associated with alarm fatigue. Intensive care nurses working on ABC shift patterns and in the public sector experienced high levels of alarm fatigue. Conclusions: The findings highlight the necessity of nursing administrators to develop alarm management policies and training courses for intensive care nurses. Nursing education policymakers should incorporate alarm fatigue and burnout concepts into nursing curricula. Relevance to Clinical Practice: This study reveals how alarm fatigue adversely affects nurses' professional quality of life in intensive care settings. It provides evidence for targeted interventions to reduce alarm fatigue and improve nurse well‐being.

PURPOSE: Thyroid cancer (TC) represents a growing global health burden with a significant increase in worldwide incidence. However, there are no comprehensive reports on TC incidence and its trend in Jordan to complement worldwide reports. This study investigates the incidence of TC in Jordan for the period 2000-2019. METHODS: Data were extracted from the Jordan Cancer Registry. Age- and sex-specific incidence rates, as well as overall age-standardized incidence rates (ASIRs), were computed and analyzed. ASIRs were computed using direct age standardization to the WHO world standard population. RESULTS: In the period between 2000 and 2019, 3,857 new cases of TC were reported in Jordan; 23.9% occurred in males and 76.1% in females, with a male-to-female ratio of 1:3. Papillary TC constituted 79.2% of cases followed by follicular (9.2%) and medullary (2%) cancers. ASIR rose from 1.7 per 100,000 in 2000 to 5.6 per 100,000 in 2019 ( P < .001) with an average ASIR of 4.1 (1.4 in males and 6.3 in females). The annual increase in ASIR was statistically significant in most years. The highest ASIR was in women aged 30-39 years. Mean incidence age was approximately 42.9 years (range, 2-90). Almost half of the patients were aged 30-49 years. CONCLUSION: This study represents the most comprehensive and updated epidemiologic analysis of TC in Jordan, highlighting a significant rise in incidence between 2000 and 2019, particularly among women aged 30-39 years. By providing long-term national data, this study contributes to a wider framework aimed at improving cancer surveillance, contextualizing global TC trends, and guiding future research in Jordan, where national cancer control strategies remain an evolving priority.

Introduction: This study assesses smoking prevalence among medical students at the University of Jordan and aims to identify key demographic, psychosocial, and behavioral factors linked to smoking initiation. Methods: A cross-sectional survey, conducted between 13 December 2023 and 15 March...

Background: The integration of generative artificial intelligence (AI) tools into nursing practice has accelerated documentation processes but it has also raised concerns regarding the completeness, accuracy, and clinical safety of AI-generated care plans. Despite the growing use of tools like ChatGPT, Gemini, and PopAI in clinical and academic settings, no validated instrument currently exists to assess the quality of such documentation across the nursing process. Objective: This study aimed to develop and validate the Nursing Process Evaluation Tool (NPET), a multidimensional instrument designed to assess the quality of AI-generated nursing documentation within the ADPIE (Assessment, Diagnosis, Planning, Implementation, Evaluation) framework. Methods: A two-phase cross-sectional study was conducted. Phase I focused on item development and content validation via two rounds of expert review ( n = 23). Phase II evaluated the NPET’s psychometric properties by assessing 64 AI-generated nursing care plans based on eight clinical scenarios using eight AI models. A total of 368 individual expert ratings were yielded. Reliability (Cronbach’s α, ICC), content and construct validity (I-CVI, S-CVI/Ave, exploratory factor analysis), and comparative model performance (repeated-measures ANOVA with Tukey post hoc tests) were analyzed. Results: The NPET demonstrated strong content validity (S-CVI/Ave = 0.88) and excellent internal consistency (α = 0.85–0.94 across domains). Inter-rater reliability was high (ICC_average = 0.85–0.94). Exploratory factor analysis supported the proposed structure: four domains were unidimensional, while the Assessment domain revealed two interpretable factors. Although the overall ANOVA did not reveal statistically significant differences among AI models (F (7, 360) = 1.57, p = 0.144, ω² = 0.01), descriptive trends and post hoc tests showed that paid models consistently outperformed free versions. PopAI Paid achieved the highest mean NPET score (M = 3.44 on a 4-point scale), followed by ChatGPT Paid (M = 3.37), while Microsoft Copilot scored the lowest (M = 2.99). The largest pairwise difference—between PopAI Paid and Copilot—yielded a moderate-to-large effect size (Cohen’s d = 0.60). Conclusion: The NPET is a valid and reliable tool for evaluating the quality of AI-generated nursing care plans. While the overall ANOVA did not yield statistically significant differences across AI models, the consistently high performance across tools and meaningful differences observed in descriptive and post hoc comparisons support the tool’s utility in nursing education, clinical auditing, and AI benchmarking. Future research should explore its application in real-world documentation and monitor its adaptability to evolving AI technologies.

Chronic glucocorticoid (GC) therapy is common in patients with rheumatic medical diseases (RMD). However, long-term use of GCs can be associated with significant adverse effects. This study aims to determine the prevalence of GC use among patients with RMD and understand their perceptions of GC use and safety. This cross-sectional survey was conducted at a tertiary academic university hospital in Amman, Jordan. RMD patients were anonymously approached while awaiting rheumatology clinic appointments. Convenience sampling was employed, with enrollment taking place between January and September 2021. Of 500 participants, 315 (63%) reported current (171/315, 54.3%) or past (144/315, 45.7%) use of GCs, primarily prescribed for RMD (267/315, 84.7%). Most (270/315, 85.7%) used GCs orally, with the majority taking them daily (266/315, 84.4%). A small percentage (33/315, 10.5%) used GCs only as needed. Long-term use was common, with 57.8% (182/315) reporting use for years and 84.1% (265/315) adhering to doctor recommendations. Perception-wise, 69.5% (219/315) of GC users believed in its efficacy, whereas 78.7% (248/315) considered it unsafe, compared to 51.9% (96/185) of non-users. Awareness of side effects was higher among GC users (219/315, 69.5%) than non-users (79/185, 42.7%), with weight gain being the most reported side effect. Media and personal research were the primary information sources for GC users (193/315, 61.3%). Side effects were reported by 110/315 (34.9%) GC users, with weight gain as the most common (47%). Both perceived efficacy and safety of GC were positively and significantly correlated with educational level (p = .005, p = .000, respectively) and monthly income (p = .044, p = .000, respectively). GC use is prevalent among Jordanian patients with RMDs, with perceptions of efficacy and safety strongly influenced by education and income. Enhanced patient education on the side effects of GC is crucial for improving treatment adherence and outcomes.

Objectives: Musculoskeletal pain is recognized as a major occupational health hazard among dentists worldwide. Physical exertion at work plays a major role in the development of musculoskeletal problems. There is growing evidence that work-related psychosocial factors may also contribute to musculoskeletal pain. Therefore, the aim of this study was to investigate the prevalence of work-related musculoskeletal pain and its demographic, physical, and psychosocial risk factors among dentists in Jordan. Methods: This cross-sectional study utilized a validated self-administered questionnaire distributed to 272 currently working Jordanian dentists. The questionnaire consisted of four sections: sociodemographic information, work physical characteristics, work-related psychosocial factors, and musculoskeletal pain assessment. Results: Fifty-one percent (51%) of Jordanian dentists reported experiencing musculoskeletal pain in the last 12 months. Neck pain (42%), followed by shoulder pain (40%), and lower back pain (40%), were the most common among dentists in Jordan. Increasing age, being a female, and obesity were significantly associated with musculoskeletal pain while smoking, exercise, and specialty level did not. A positive correlation between musculoskeletal pain and unfavorable physical characteristics at work was identified. In addition, increased work stress was positively correlated with musculoskeletal pain. Conclusion: This study showed a high prevalence of musculoskeletal pain among Jordanian dentists. Increasing dentists’ and healthcare authorities’ awareness about factors associated with work-related musculoskeletal pain is needed. Educational programs targeting dentists at risk of developing musculoskeletal pain might be of potential preventive value.