Scottish Medical Journal最新文献

BackgroundEffective communication and shared decision-making are essential for optimising urological care, making informed decisions, and improving patient outcomes. The integration of artificial intelligence (AI) in urology has the potential to act as a supportive tool in this process. This review aims to evaluate how AI-based tools support and enhance patient-provider communication and shared decision-making within urological care.MethodsFollowing PRISMA 2020 guidelines, a systematic search was performed using Cochrane, EMBASE, MEDLINE, and Scopus for literature published between 2019 and 2024. Search terms included 'Artificial Intelligence', 'Urology', 'Shared Decision-Making', and 'Communication'. Studies were screened using our predefined inclusion and exclusion criteria. Three primary themes were identified, through which the studies were analysed.ResultsOf 807 identified studies, 14 were appropriate for inclusion. Only 14 studies met criteria because most excluded articles did not evaluate AI tools designed for communication, health literacy, or shared decision-making. AI-driven tools, particularly large language models (LLMs), show the potential to reduce knowledge gaps for diverse literacy levels and improve patient comprehension. These aids may improve the readability of complex medical content and translate information with cultural sensitivity. AI may also enhance communication between patients and healthcare providers by automating repetitive tasks, such as responding to frequently asked questions. However, AI has limitations, with different LLMs displaying variable levels of effectiveness and accuracy across urological conditions.ConclusionsThe integration of AI has the potential to enhance communication and promote shared decision-making in urology. However, patients should use AI as a complement to physicians rather than a replacement. To confidently determine their role and ensure AI output accuracy, further studies, including validation against clinical standards and real-world accuracy are required.

BackgroundTo deliver and evaluate two Emergency Urology Skills Training (EUST) courses in Ethiopia, aimed at equipping surgical and urology residents with hands-on skills and confidence in managing urological emergencies in resource-limited settings.MethodsTwo one-day, practical training courses were held in Hawassa and Addis Ababa in November 2024 and May 2025. Pre-course questionnaires assessed delegates' baseline confidence, prior training and the utility of a pre-course manual. A blended curriculum comprising didactic lectures, skill stations and one-to-one mentorship was delivered by a collaborative team of local and international faculty. Post-course evaluations measured improvements in knowledge, confidence and satisfaction.ResultsTwenty-three participants from each centre completed matched pre- and post-course multiple-choice questionnaires assessing knowledge of emergency urology procedures. Pre-course exposure to structured skills training was limited (≤30%). Both groups showed statistically significant improvements in post-course scores (Hawassa: p = 0.002; Addis Ababa: p = 0.007). Self-rated confidence and knowledge improved significantly (p < 0.05) in six of eight core procedures.ConclusionsThe EUST model effectively improved trainees' confidence and procedural competence in emergency urology. With adequate support, this model is scalable and applicable to other resource-limited countries seeking to strengthen urological emergency care and their training capacity.

BackgroundPredicting isocitrate dehydrogenase (IDH) status is crucial in glioma management. Conventional MRI (cMRI) has limitations, but the clinical translation of radiomics and machine learning (ML) is often limited by single-center datasets and poor model interpretability.PurposeTo develop and validate an interpretable, multicenter ML model integrating cMRI with functional sequences (DWI and PWI) for predicting IDH status in gliomas.Material and MethodsThis retrospective study included 180 patients from four institutions (150 training, 30 external test). Radiomics features were extracted from cMRI (T1WI, T2WI, FLAIR, T1CE), DWI, and DSC-PWI (CBV maps). After feature selection, multiparametric MRI-based fusion radiomics models were built and compared using three ML algorithms across four segmentation strategies. The optimal model was explained using SHapley Additive exPlanation (SHAP).ResultsThe full-modality model (cMRI + DWI + PWI) with 3Dmodified segmentation achieved the best performance, with area under the curve of 0.840 (training) and 0.810 (external test). Incorporating functional sequences significantly improved prediction over cMRI alone. SHAP analysis identified key predictive features and provided individualized visual explanations for model decisions.ConclusionThe developed ML-SHAP model, integrating conventional and functional MRI, reliably predicts IDH status and demonstrates generalizability across multiple centers. This interpretable tool shows potential for supporting preoperative molecular diagnosis in glioma.

In the assessment of cerebrospinal fluid flow disorders such as aqueductal stenosis, Chiari malformation, and post-operative complications, conventional imaging often falls short in resolving subtle anatomical and dynamic nuances. This case series explores the value of integrating high-resolution flow-sensitive and flow-compensated magnetic resonance imaging sequences to enhance diagnostic precision in challenging scenarios. Across five diverse cases, the combined approach revealed subtle aetiologies like prepontine adhesions, fourth ventricle outlet obstruction, aqueductal stenosis and minute dural breaches - all which were undetectable with routine conventional imaging.

Background and aimsNational Health Service waiting lists for some routine procedures are currently very long, and patients' conditions may change during the wait, requiring reassessment before surgery can be offered. The objective of this study was to implement a septoplasty waiting list validation process incorporating the nasal obstruction symptom evaluation (NOSE) questionnaire to help streamline patient selection for septoplasty.Methods and resultsA telephone consultation incorporating the NOSE questionnaire was designed to validate the local septoplasty waiting list. NOSE scores were evaluated pre- and post-operatively. A total of 112 patients met the inclusion criteria. Thirty-three patients were removed from the waiting list on initial consultation, of whom the majority had a NOSE score ≤30 or had not trialled maximal medical management. In total, 86% of patients removed from the waiting list had not sought any further ENT input at the 4-year follow-up period. The mean NOSE score improved between pre- and post-operative evaluations (57.7 vs. 26.9, respectively, p < 0.0001). There was a strong correlation between higher pre-operative NOSE scores and improvement in NOSE scores post-operatively (r = 0.57293).ConclusionsThe NOSE score is an effective way to validate septoplasty waiting lists and assists in appropriate patient selection for septoplasty.

BackgroundPeritoneal carcinomatosis (PC) represents an advanced stage of intra-abdominal malignancies with poor prognosis. Cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) offers promising outcomes in selected patients. This study evaluated survival and prognostic factors in patients undergoing CRS-HIPEC for malignancy-associated PC.MethodsA retrospective analysis was performed on 116 patients treated with CRS-HIPEC between 2015 and 2023. Data included demographics, tumor origin, peritoneal cancer index (PCI), completeness of cytoreduction (CC) score, complications, and survival outcomes. Kaplan-Meier and Cox regression analyses were used.ResultsThe mean age was 55.2 years; 61.2% were female. Common primary tumors were colorectal (34.5%), gastric (20.7%), ovarian (17.2%), and appendiceal (16.4%). Mean PCI was 11.9, and complete cytoreduction (CC-0) was achieved in 89.7%. Overall survival was highest in appendiceal (76.9 months) and lowest in gastric cancer (24.7 months). Median disease-free and overall survival were 36 and 43.9 months, respectively. Postoperative complications occurred in 31.8%; mortality was 5.17%. High PCI and incomplete cytoreduction were independently associated with worse survival.ConclusionCRS-HIPEC provides encouraging survival in selected PC patients, especially with low PCI and complete cytoreduction. Careful patient selection remains key to optimizing outcomes.

Background and AimsTo determine which factors were associated with and predictive of an advanced stage at diagnosis and emergency mode of presentation in colorectal cancer patients.Methods and ResultsA total of 587 consecutive patients discussed at multidisciplinary team meetings between January 2017 and December 2019 at an acute hospital in Scotland were included. The effect of predefined variables on stage and mode of presentation was analysed using logistic regression. 55.4% of patients had advanced disease and 19.7% of patients presented as an emergency: these were more likely to have advanced disease (odds ratio (OR) 1.64, 95% confidence interval (95%CI) 1.02-2.62, p = 0.04) compared to the 22.3% diagnosed at screening (OR 0.33, 95%CI 0.21-0.52, p < 0.001). Individuals with affected first-degree relatives were more likely to present with advanced disease (OR 2.39, 95%CI 1.06-5.40, p = 0.04). The association of deprivation and tumour site with advanced disease at presentation was significant (p = 0.04 and p = 0.013, respectively). The likelihood of presenting as an emergency increased with age (OR 1.03, 95%CI 1.01-1.05, p = 0.003) and with advanced disease (OR 2.15, 95%CI 1.37-3.38, p < 0.001). Again, the relationship between tumour site and emergency presentation was significant (p = 0.002).ConclusionFurther efforts are required to increase screening uptake, particularly amongst higher risk groups, to detect disease at an early stage and reduce the number of patients presenting as emergencies.

BackgroundThe aim of this study was to assess the efficacy of post-stroke rehabilitation and to determine the influence of differing rehabilitation program components on changes in quality of life (QoL).Methods120 patients diagnosed with stroke were included in this study. The sample comprised 53 males and 67 females, exhibiting a meantime since the event of 77.7 ± 70.5 days (range:30-365 days). Patient sociodemographic data, stroke characteristics, and received rehabilitation services were documented upon admission. Hemiplegic patients' motor development was assessed using the Brunnstrom stages. QoL was assessed using the stroke-specific quality of life scale (SS-QOL).ResultsInpatient rehabilitation lasted an average of 59.71 ± 19.56 days (range:30-70 days). A comparison of SS-QOL scale scores at admission and discharge among rehabilitation patients revealed improvements in both the overall mean and all subscales. Statistically significant improvements were found in all rehabilitation sub-dimensions. Cognitive rehab significantly improved patients' social, language, vision, cognitive, and personality functioning. Analysis revealed significant enhancements in mood and activity among subjects undergoing lower extremity robotic rehabilitation and balance laboratory treatment. Hand support showed significant improvement across all measured sub-dimensions.ConclusionsOur research facilitated a comprehensive assessment of stroke-induced impairments, demographic variables, and individualized treatment modalities influencing QoL, as measured by a dedicated SS-QOL scale.