Tag: nevin manimala

Int J Prosthodont. 2025 Sep 16;0(0):1-28. doi: 10.11607/ijp.9306. Online ahead of print.

ABSTRACT

PURPOSE: This study aims to assess the accuracy of one-piece implant placement using stackable surgical guides by means of a novel stent defined as ‘number guide’ screwed onto the fixed base template in order to improve the intra-surgical registration of the scan bodies.

MATERIAL AND METHODS: A digital workflow was used for one-piece implant planning and placement. Participants were selected based on specific criteria, and stackable guides were used for fully guided implant placement with immediate provisional loading. The accuracy of implant placement was assessed by comparing pre- and post-intervention data registered with the aid of the number guide.

RESULTS: Ten participants were treated with forty-seven one-piece implants placed in the maxilla and mandible. All implants achieved adequate primary stability, allowing immediate loading. Accuracy was measured by linear and angular deviations, showing greater precision in the mandible. Implant characteristics yielded statistically significant differences in terms of accuracy.

CONCLUSIONS: Fully guided digital workflow ensured precise implant placement and immediate provisional loading. The additional use of the number guide allowed effective accuracy assessments of the system in terms of linear and angular deviations, highlighting its potential to assess post-operative implant placement.

PMID:40957069 | DOI:10.11607/ijp.9306

JMIR Med Educ. 2025 Sep 16;11:e75916. doi: 10.2196/75916.

ABSTRACT

BACKGROUND: Effective diabetes management requires behavioral change support from primary care providers. However, general practitioners (GPs) often lack training in patient-centered communication methods such as motivational interviewing (MI), especially in time-constrained settings. While brief MI offers a practical alternative, evidence on its impact among GPs and patient outcomes remains limited.

OBJECTIVE: This study aimed to evaluate the effectiveness of a structured MI educational program for GPs and GP trainees on their MI knowledge and confidence, and its impact on clinical outcomes among patients with type 2 diabetes in primary care settings.

METHODS: A mixed methods study was conducted using a before-and-after two-group design with quantitative assessments of GPs’ knowledge and patients’ biomarkers, supplemented by qualitative interviews. The intervention group (n=35) received a 4-hour interactive MI workshop, optional web-based modules, and brief MI guides. The control group received standard care. A total of 149 and 167 patients with diabetes were included in the study and control groups, respectively.

RESULTS: A paired-sample t test was conducted to evaluate the impact of the MI course on the learners’ knowledge. There was a statistically significant difference in the knowledge test scores from Time 1 (mean 11.46, SD 3.48) to Time 2 (mean 15.04, SD 2.35), t28= -7.74; P<.001 (2-tailed). The mean increase in knowledge score was 3.57 (SD 2.44), with a 95% CI of 2.62 to 4.52, indicating a large and statistically significant effect. The eta-squared statistic indicated a large effect size (eta-squared=0.85). Patients in the intervention group had greater improvements in HbA1c (mean difference= -0.50, 95% CI -0.91 to -0.09; P=.02) and diastolic blood pressure (mean difference= -5.96 mmHg, 95% CI -8.66 to -3.25; P<.001) compared to controls. Qualitative feedback highlighted the usefulness of brief MI, along with challenges in mastering advanced techniques and time constraints.

CONCLUSIONS: The MI educational program improved GP trainees’ MI knowledge and patient outcomes. Brief MI appears feasible in primary care but requires ongoing support for skill development and implementation.

PMID:40957062 | DOI:10.2196/75916

JMIR Public Health Surveill. 2025 Sep 16;11:e75291. doi: 10.2196/75291.

ABSTRACT

BACKGROUND: Incident HIV infection is a critical indicator of an ongoing epidemic, particularly in high-burden regions such as Liangshan Yi Autonomous Prefecture in China, where HIV prevalence exceeds 1% in 4 key counties (Butuo, Zhaojue, Meigu, and Yuexi). Identifying spatial clusters and drivers of recent infections is essential for implementing targeted interventions. Despite advancements in geospatial analyses of HIV prevalence, studies identifying drivers of incident HIV clustering remain limited, especially in low-resource settings.

OBJECTIVE: This study aims to identify spatial clusters of recent HIV infections and investigate potential driving factors in 4 key counties of the Liangshan Yi Autonomous Prefecture to inform targeted intervention strategies.

METHODS: From November 2017 to June 2018, we identified 246 (4.42%) recent HIV infection cases from 5555 newly diagnosed cases through expanded testing of the whole population in 4 key counties of Liangshan Yi Autonomous Prefecture. Recent infection cases were confirmed using limiting antigen avidity enzyme immunoassays or documented seroconversion within 6 months. The spatial distribution of incident HIV infection cases was analyzed using kernel density. Potential drivers, including population density, HIV prevalence, elevation, nighttime light index, urban proximity, and antiretroviral therapy (ART) coverage, were analyzed. The spatial lag regression model was used to identify factors associated with clustering of recent infection cases. The Geodetector q-statistic was used to quantify nonlinear interactive effects among these factors.

RESULTS: Significant spatial autocorrelation was observed in the distribution of recent HIV cases (Moran I=0.11; P<.01). Six spatial clusters were identified, and all were located near urban centers or major roads. Furthermore, 5 factors were identified by the spatial lag regression model as being significantly correlated with the clustering of recent HIV infection cases, including population density (β=0.59; P<.001), HIV prevalence (β=0.02; P<.001), distance to local urban area (β=-3.10; P=.01), SD of elevation (β=-0.15; P=.02), and ART coverage rate (β=183.80; P<.01). Geodetector analysis revealed strong interactive effects among these 5 factors, with population density and HIV prevalence exhibiting the largest interactive effect (q=0.69).

CONCLUSIONS: This study reveals that besides HIV prevalence, urbanization-related factors (population density and proximity to urban area) and transportation accessibility drive incident HIV clustering in Liangshan Yi Autonomous Prefecture. Paradoxically, higher ART coverage was associated with increased transmission, suggesting the need for integrated prevention strategies beyond ART expansion. Furthermore, the township-level geospatial approach provides a valuable model for pinpointing transmission hot spots and tailoring interventions in high-burden regions globally.

PMID:40957018 | DOI:10.2196/75291

JMIR Form Res. 2025 Sep 16;9:e72331. doi: 10.2196/72331.

ABSTRACT

BACKGROUND: Digital health measurement offers opportunities to address several primary health care challenges, but health care professionals encounter significant implementation barriers. Therefore, resources need to be developed to facilitate the integration of digital health measurement into daily practice.

OBJECTIVE: We aim to identify the most appropriate format and content for an intervention to support primary health care professionals in adopting digital health measurement. In addition, we describe and reflect on the development process.

METHODS: We used a participatory action research approach as well as user-centered design principles. A total of 19 primary health care professionals from 4 disciplines-physical therapy, occupational therapy, speech and language therapy, and general practitioner practice assistance-participated in intervention development as end users. External experts were consulted to broaden perspectives. Data were collected across 3 iterative stages (concept, design, and testing and trials) between January 2022 and December 2023 during cocreative meetings, individual interviews, focus group discussions, usability testing, and prototype use in daily practice. Data were analyzed using content analysis and descriptive statistics.

RESULTS: A web-based, stepwise intervention combining theoretical information, practical aids, examples, and experiences proved most suitable. Key features were concise content, intuitive and attractive graphic design, and flexible navigation and functionalities. Iterative improvements led to an increase in usability ratings from “okay” to “good to excellent.”

CONCLUSIONS: Different health care disciplines benefit from similar support strategies; yet, this requires a careful balancing of intervention design and content. Combining participatory action research and user-centered design principles was useful to tailor the intervention to end users’ daily routines. The described development process offers a replicable framework for creating support strategies for digital health measurement in various health care settings.

PMID:40957009 | DOI:10.2196/72331

J Med Internet Res. 2025 Sep 16;27:e66667. doi: 10.2196/66667.

ABSTRACT

BACKGROUND: Determining effective nurse staffing levels is crucial for ensuring quality patient care and operational efficiency within hospitals. Traditional workload prediction methods often rely on professional judgment or simple volume-based approaches, which can be inaccurate. Machine learning offers a promising avenue for more data-driven and precise predictions, by using historical nursing workload data to forecast future patient care requirements, which could help with staff planning while also improving patient outcomes and nurse well-being.

OBJECTIVE: This methodological study aimed to use nursing activity data, specifically LEP (Leistungserfassung in der Pflege; “documentation of nursing activities”), to predict the future workload requirements using machine learning techniques.

METHODS: We conducted a retrospective observational study at the University Hospital of Zürich, using nursing workload data for inpatients across eight wards, collected between 2017 and 2021. Data were transformed to represent nursing workload per ward and shift, with 3 shifts per day. Variables used in modeling included historical workload trends, patient characteristics, and upcoming operations. Machine learning models, including linear regression variants and tree-based methods (Random Forest and XGBoost), were trained and tested on this dataset to predict workload 72 hours in advance, on a shift-by-shift basis. Model performance was assessed using mean absolute error and mean absolute percentage error, and results were compared against a baseline of assuming no change in workload from the time of prediction. Prediction accuracy was further evaluated by categorizing future workload changes into decreased, similar, or increased workload relative to current shift levels.

RESULTS: Our findings demonstrate that machine learning models consistently outperform the baseline across all wards. The best-performing model was the lasso regression model, which achieved an average improvement in accuracy of 25.0% compared to the baseline. When used to predict upcoming changes in workload levels, the model achieved strong classification performance, giving an average area under the receiver operating characteristic curve of 0.79 and precision values between 66.2% and 75.3%. Crucially, the model severely misclassified-predicting an upcoming increase as a decrease, and vice versa-in just 0.17% of cases, highlighting potential reliability for using the model in practice. Key variables identified as important for predictions include historical shift workload averages and overall ward workload trends.

CONCLUSIONS: This study suggests the potential of machine learning to enhance nurse workload prediction, while highlighting the need for refinement. Limitations due to potential discrepancies between recorded nursing activities and the actual workload highlight the need for further investigation into data quality. To maximize impact, future research should focus on: (1) using more diverse data, (2) more advanced machine learning architecture that performs time-series modeling, (3) addressing data quality concerns, and (4) conducting controlled trials for real-world evaluation.

PMID:40956986 | DOI:10.2196/66667

JMIR Infodemiology. 2025 Sep 16;5:e67473. doi: 10.2196/67473.

ABSTRACT

BACKGROUND: Chronic pain, affecting 30.3% of the global population, constitutes a major public health and social challenge. It is associated with disability, emotional distress, and diminished quality of life. Conditions, such as fibromyalgia, headache, paraplegia, neuropathy, and multiple sclerosis are characterized by persistent pain and limited social and medical understanding. This contributes to patient isolation and increases mental health burden. In recent years, social media, particularly X (formerly Twitter), has emerged as a key space for analyzing health-related perceptions and experiences. Its massive use, spontaneity, and broad reach have made these platforms a valuable source for infodemiological research.

OBJECTIVE: This study aims to analyze posts on X concerning fibromyalgia, headache, paraplegia, neuropathy, and multiple sclerosis, as well as characterize the profile of users involved in these conversations, identify prevalent topics, measure public perception, evaluate treatment efficacy, and detect discussions related to the most frequent nonmedical issues.

METHODS: A total of 72,874 tweets in English and Spanish containing the selected keywords were collected between 2018 and 2022. A manual review of 2500 tweets was conducted, and the larger subset was automatically classified using natural language processing methods based on the BERTweet model, previously fine-tuned for content analysis on social media platforms. Subsequently, tweets related to chronic pain conditions were analyzed to examine user types, disease origin, and both medical and nonmedical content.

RESULTS: Of the total tweets collected, 55,451 (76.1%) were classifiable. The most active users were health care professionals and institutions. The primary perceived etiology was pharmacological, and higher treatment efficacy was noted in neuropathy, paraplegia, and multiple sclerosis. Regarding nonmedical content, there were more tweets related to the definition and understanding of the disease.

CONCLUSIONS: Social media platforms, such as X, are playing a crucial role in the dissemination of information on chronic pain. Discussions largely focus on the available treatments and the need to enhance public education, using these platforms to correct misconceptions and provide better support to patients.

PMID:40956980 | DOI:10.2196/67473

Adv Skin Wound Care. 2025 Sep 5. doi: 10.1097/ASW.0000000000000357. Online ahead of print.

ABSTRACT

OBJECTIVE: The relationship between the knowledge levels of agriculture faculty students regarding skin cancer risk and their protective behaviors was examined.

METHODS: This study is a descriptive and cross-sectional research conducted to evaluate the knowledge and behaviors of agriculture faculty students regarding skin cancer and sun protection. Research data were collected with the Personal Information Form, Skin Cancer and Sun Knowledge Scale, and Sun Protection Behavior Scale.

RESULTS: The mean scores obtained on the Skin Cancer and Sun Knowledge Scale were 10.95 ± 3.02, and the mean scores of the Sun Protection Behavior Scale were 24.79 ± 6.95. A statistically significant relationship was found between sun protection behaviors and various factors such as sex, hair color, skin color, and mole-checking methods (P < .05). More specifically, students who described their skin color as dark had lower scores in sun protection, which was a statistically significant difference (P < .05). In addition, a weak negative relationship was discovered between the subscale of skin cancer risk factors and the subscale of sun avoidance (P < .05).

CONCLUSIONS: The study indicates that agriculture faculty students possess insufficient knowledge about skin cancer and sun protection, and their preventive behaviors are inadequate. It is important to develop educational programs and implement strategies to provide students with the necessary behaviors regarding skin cancer and sun-related health.

PMID:40956979 | DOI:10.1097/ASW.0000000000000357

Adv Skin Wound Care. 2025 Sep 16. doi: 10.1097/ASW.0000000000000363. Online ahead of print.

ABSTRACT

OBJECTIVE: The incidence of skin cancer is increasing worldwide every day. This study was conducted to evaluate body image perception and self-esteem in patients with skin cancer.

METHODS: This prospective, cross-sectional, relationship-seeking, and descriptive research design study was carried out with 320 patients diagnosed with skin cancer to assess their body image perception and self-esteem levels. Data were collected between December 2023 and September 2024 at a city hospital. The data collection tools used in the study included the Patient Information Form, Body Image Scale, and Rosenberg Self-Esteem Scale (RSES).

RESULTS: The average age of the patients participating in the study was found to be 63.9 ± 18.4 years. The average scores of the patients on the Body Image Scale and the RSES were 129.6 ± 26.1. It was found that the patients had a low body image perception. The average score on the RSES was 2.6 ± 1.5, indicating that the self-esteem level of the patients was at a moderate level. A statistically significant relationship was found between the scores on the Body Image Scale and the RSES (P<.01). As the patients’ positive body image perception increased, their self-esteem also improved.

CONCLUSIONS: It was determined that patients with skin cancer had a low body image perception and moderate self-esteem levels.

PMID:40956973 | DOI:10.1097/ASW.0000000000000363

Can Assoc Radiol J. 2025 Sep 16:8465371251371567. doi: 10.1177/08465371251371567. Online ahead of print.

ABSTRACT

OBJECTIVE: To assess the operational and environmental benefits of using an abbreviated protocol for hepatocellular carcinoma (HCC) surveillance.

METHODS: This IRB-approved retrospective single-center quality improvement study evaluated time, energy use, and appointment access. Inclusion criteria were HCC surveillance MRIs with either a full or abbreviated imaging protocol. Exclusion criteria were other abdominopelvic MR protocols or incomplete studies. DICOM time data were extracted via Quantivly and validated with 10 prospective time studies. Exam times from PACS images were cross-referenced with DICOM data to identify and resolve extraction outliers. Power logs from 10 exams per protocol were used to quantify energy and greenhouse gas emissions. Schedule logs assessed appointment volume changes. Mean times (±SD) and energy (±SD) were reported, and Welch’s t-test determined statistical significance (P < .05).

RESULTS: Exam times for 487 MRIs (318 abbreviated, 169 full protocol) were analyzed, with 67 excluded. The mean duration of exam time for the abbreviated protocol was 12.0 minutes (SD: 4.3), compared with 29.7 minutes (SD: 8.8) for the full protocol (mean difference, 17.7 minutes; P < .0001). The mean energy for the abbreviated protocol was 4.7 kWh (SD: 0.6), compared with 11.7 kWh (SD: 1.3) for the full protocol (mean difference, 7.0 kWh; P < .0001). Across 318 abbreviated exams, estimated savings totaled 2226 kWh and 1494.6 kg CO2eq. Despite time savings, MRI appointment volume and scanner access remained unchanged.

CONCLUSION: Abbreviated HCC surveillance MRIs cut scan time, energy use, and carbon emissions by 60%, but scheduling complexities precluded increased MRI appointments.

PMID:40956971 | DOI:10.1177/08465371251371567

Inquiry. 2025 Jan-Dec;62:469580251372362. doi: 10.1177/00469580251372362. Epub 2025 Sep 16.

ABSTRACT

This systematic review and meta-analysis evaluated how exergaming (EXG) compares with various conventional physical therapies in improving balance and reducing fall risk among prefrail and frail older people. We searched 6 databases PubMed, Medline, CINAHL Complete, Scopus, the Cochrane Library, and Web of Science up to April 2025. Study quality and evidence certainty were appraised using PRISMA, TESTEX, Rob 2, and GRADE. For meta-analysis, Hedge’s g effect sizes were computed for balance and fall risk outcomes. We chose fixed- or random-effects models and conducted subgroup analyses based on therapy dosage (sessions per week and minutes per session). The protocol is registered in PROSPERO (CRD420251009891). From 2434 records, 10 RCTs (n = 400; mean and standard deviation age 75.7 ± 5.9 years) met inclusion criteria. Overall and subgroup meta-analyses (4 each) showed significant EXG benefits for the Mini-BESTest (P < .01), Timed Up-and-Go (TUG; P < .05) and Fall Efficacy Scale-International (FES-I; P < .05). No statistically significant change was found for the Berg Balance Scale (BBS; P = .05). When stratifying by dosage, EXG outperformed controls in TUG specifically for protocols with fewer than 3 sessions/week and under 50 min/session (P < .01). Dosage did not significantly influence FES-I outcomes. EXG is an alternative therapy that improves balance by reducing the fall risk, as measured by the Mini-BESTest, TUG, and FES-I, compared with conventional physical therapies (ie, physiotherapy, balance training, strength training, aerobic training, multicomponent training). Notably, protocols with <3 weekly sessions of <50 min each yielded the most pronounced TUG improvements.

PMID:40956936 | DOI:10.1177/00469580251372362