Tag: nevin manimala

Traffic Inj Prev. 2024 Oct 15:1-10. doi: 10.1080/15389588.2024.2404713. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aims to develop and validate a novel deep-learning model that predicts the severity of pedestrian-vehicle interactions at unsignalized intersections, distinctively integrating Transformer-based models with Multilayer Perceptrons (MLP). This approach leverages advanced feature analysis capabilities, offering a more direct and interpretable method than traditional models.

METHODS: High-resolution optical cameras recorded detailed pedestrian and vehicle movements at study sites, with data processed to extract trajectories and convert them into real-world coordinates via precise georeferencing. Trained observers categorized interactions into safe passage, critical event, and conflict based on movement patterns, speeds, and accelerations. Fleiss Kappa statistic measured inter-rater agreement to ensure evaluator consistency. This study introduces a novel deep-learning model combining Transformer-based time series data capabilities with the classification strengths of a Multilayer Perceptron (MLP). Unlike traditional models, this approach focuses on feature analysis for greater interpretability. The model, trained on dynamic input variables from trajectory data, employs attention mechanisms to evaluate the significance of each input variable, offering deeper insights into factors influencing interaction severity.

RESULTS: The model demonstrated high performance across different severity categories: safe interactions achieved a precision of 0.78, recall of 0.91, and F1-score of 0.84. In more severe categories like critical events and conflicts, precision and recall were even higher. Overall accuracy stood at 0.87, with both macro and weighted averages for precision, recall, and F1-score also at 0.87. The variable importance analysis, using attention scores from the proposed transformer model, identified ‘Vehicle Speed’ as the most significant input variable positively influencing severity. Conversely, ‘Approaching Angle’ and ‘Vehicle Distance from Conflict Point’ negatively impacted severity. Other significant factors included ‘Type of Vehicle’, ‘Pedestrian Speed’, and ‘Pedestrian Yaw Rate’, highlighting the complex interplay of behavioral and environmental factors in pedestrian-vehicle interactions.

CONCLUSIONS: This study introduces a deep-learning model that effectively predicts the severity of pedestrian-vehicle interactions at crosswalks, utilizing a Transformer-MLP hybrid architecture with high precision and recall across severity categories. Key factors influencing severity were identified, paving the way for further enhancements in real-time analysis and broader safety assessments in urban settings.

PMID:39405419 | DOI:10.1080/15389588.2024.2404713

J Hosp Palliat Nurs. 2024 Oct 14. doi: 10.1097/NJH.0000000000001068. Online ahead of print.

ABSTRACT

Polypharmacy is commonly encountered by providers caring for patients with medically complex and palliative care needs in many settings. The purpose of this quality improvement project was to measure the impact of an evidence-based educational deprescribing intervention on polypharmacy rate and provider confidence and knowledge in the nursing home. We invited providers working in 52 nursing homes to attend a 1-hour-long educational deprescribing session. Twenty-one nurse practitioners and 1 physician assistant across 11 states participated in the intervention. Provider confidence level related to deprescribing improved in all categories, with statistical significance demonstrated with both paired t test and Wilcoxon signed rank test (P < .001). The polypharmacy rate 3 months after the intervention decreased more in centers where a provider had attended the training. Additional open-ended data about experiences with and barriers to deprescribing were collected and analyzed. The findings from this quality improvement project demonstrate that an educational intervention focused on providers practicing in the nursing home setting can improve deprescribing confidence and reduce polypharmacy rates. These findings may be used to implement similar deprescribing education programs for palliative care nurses and providers that prioritize goals of care for patients living with serious illness.

PMID:39405406 | DOI:10.1097/NJH.0000000000001068

J Sports Sci. 2024 Oct 15:1-8. doi: 10.1080/02640414.2024.2414361. Online ahead of print.

ABSTRACT

Motor competence (MC) is conceptually defined as a multidimensional latent construct that covers the proficient performance in motor skills and its underlying mechanisms This study aimed to statistically provide arguments that MC is a network of interconnected constructs, such as FMS, coordination, and its underlying mechanisms, which are responsible for preschoolers’ proficiency in motor tasks. Participated 102 preschoolers (65 girls, M age = 4.22 ± 0.19) who were assessed for the Test of Gross Motor Development – 2^nd edition, the Motor Competence Assessment, and the Supine-to-Stand. Data were explored using Exploratory Graph Analysis, using the EGAnet package in RStudio. A four-dimensional structure (61.2% of interactions) comprising tasks of the different protocols was underlined, in which all the nodes presented stable and adequate indexes (≥0.65; TEFI = -2.67). Four dimensions of MC were highlighted, namely Dimension 1, which combined movements for locomotor patterns; Dimension 2, comprising three process-oriented measures of object control skills to project objects; Dimension 3, which comprised of skills which require body coordination to displace body through space; and Dimension 4, composed by object control skills evaluated through product-oriented measures. For a better understanding of MC, the assessment of these different aspects that comprises MC should be considered.

PMID:39405381 | DOI:10.1080/02640414.2024.2414361

Cancer Control. 2024 Jan-Dec;31:10732748241292786. doi: 10.1177/10732748241292786.

ABSTRACT

OBJECTIVE: To investigate the impact of urinary iodine concentration (UIC) and post-stimulatory thyroglobulin (ps-Tg) levels on the therapeutic efficacy of differentiated thyroid cancer (DTC) patients after initial radioiodine therapy, and to analyze the validity of these indicators as prognostic factors.

METHODS: A total of 213 DTC patients received initial radioiodine therapy from June 2022 to September 2023. Demographic data and UIC were collected before and after therapy. Thyrotropin, thyroglobulin (Tg), and thyroglobulin antibody levels were assessed. Iodine uptake rate was measured, and therapeutic efficacy was evaluated 6 months post-therapy. Statistical tests were used for data comparison, and logistic regression analysis for response factors.

RESULTS: Post-therapy UIC and pre-post UIC difference were significantly correlated with Tg levels but not with reaching excellent response (ER) indicated by suppression of Tg levels below 0.2 ug/L. Ps-Tg levels related to therapeutic efficacy, while UIC did not correlate with outcomes. ROC curve analysis found optimal ps-Tg cut-off points for the low-intermediate and high-risk groups classified by primary tumor size, invasion, metastasis, and pathological type.

CONCLUSION: Post-treatment UIC and pre-post UIC difference correlate with ps-Tg levels. Ps-Tg levels are an associated factor for DTC, but UIC changes, despite correlation with ps-Tg, are not significantly related to outcomes and cannot be used as a prognostic factor.

PMID:39405376 | DOI:10.1177/10732748241292786

Proc Natl Acad Sci U S A. 2024 Oct 22;121(43):e2412872121. doi: 10.1073/pnas.2412872121. Epub 2024 Oct 15.

ABSTRACT

Obesity is a major public health crisis in the United States (US) affecting 42% of the population, exacerbating a spectrum of other diseases and contributing significantly to morbidity and mortality overall. Recent advances in pharmaceutical interventions, particularly glucagon-like peptide-1 (GLP-1) receptor agonists (e.g., semaglutide, liraglutide) and dual gastric inhibitory polypeptide and GLP-1 receptor agonists (e.g., tirzepatide), have shown remarkable efficacy in weight-loss. However, limited access to these medications due to high costs and insurance coverage issues restricts their utility in mitigating the obesity epidemic. We quantify the annual mortality burden directly attributable to limited access to these medications in the US. By integrating hazard ratios of mortality across body mass index categories with current obesity prevalence data, combined with healthcare access, willingness to take the medication, and observed adherence to and efficacy of the medications, we estimate the impact of making these medications accessible to all those eligible. Specifically, we project that with expanded access, over 42,000 deaths could be averted annually, including more than 11,000 deaths among people with type 2 diabetes. These findings underscore the urgent need to address barriers to access and highlight the transformative public health impact that could be achieved by expanding access to these novel treatments.

PMID:39405358 | DOI:10.1073/pnas.2412872121

PLoS One. 2024 Oct 15;19(10):e0308654. doi: 10.1371/journal.pone.0308654. eCollection 2024.

ABSTRACT

With the improvement of education level and the expansion of higher education, more students can have the opportunities to obtain better education, and the pressure of employment competition is also increasing. How to improve students’ employment competitiveness, comprehensive quality and the ability to explore paths for career planning and employment strategies has become a common concern in today’s society. Under the background of today’s informatization, the paths of career planning and employment strategies are becoming more and more informatized. The support of Internet is essential for obtaining more employment information. As a representative product of the information age, deep learning provides people with a better path. This paper conducts an in-depth study of the career planning and employment strategy paths based on deep learning in the information age. Research has shown that in the current information age, deep learning through career planning and employment strategy paths can help students solve the main problems they face in career planning education and better meet the needs of today’s society. Career awareness increased by 35% and self-improvement by 15%. This indicated that in the information age, career planning and employment strategies based on deep learning are a way to conform to the trend of the times, which can better help college students improve their understanding, promote employment, and promote self-development.This study combines quantitative and qualitative methods, collects data through questionnaires, and uses deep learning model for analysis. Control group and experimental group were set up to evaluate the effect of career planning education. Descriptive statistics and correlation analysis were used to ensure the accuracy and reliability of the results.

PMID:39405324 | DOI:10.1371/journal.pone.0308654

PLoS One. 2024 Oct 15;19(10):e0311850. doi: 10.1371/journal.pone.0311850. eCollection 2024.

ABSTRACT

In ophthalmology and otolaryngology, data collected from paired body parts are typically reformatted into categorical bilateral data structures for subsequent research. This article applies Donner’s equal correlation coefficient model and obtains nine simultaneous confidence intervals (SCI) of proportion ratios under three asymptotic statistical methods and three ways of multiplicity adjustment. The empirical coverage probability and mean interval width are evaluated through Monte Carlo simulations. A real example is used to demonstrate the proposed methods.

PMID:39405312 | DOI:10.1371/journal.pone.0311850

PLoS One. 2024 Oct 15;19(10):e0309177. doi: 10.1371/journal.pone.0309177. eCollection 2024.

ABSTRACT

BACKGROUND: Women developing preeclampsia (PE) or fetal growth restriction (FGR) during pregnancy are at higher risk for cardiovascular diseases (CVD) later in life. We aimed to analyse cardiovascular health of women 10-20 years after affected pregnancies in comparison to women after uneventful pregnancies. In addition, we assessed a potential long-term effect of the NO-donor pentaerythrityl tetranitrate (PETN).

METHODS: Women 10-20 years after severe PE, including women receiving PETN during pregnancy and matched controls were recruited and assessed for baseline clinical data and cardiovascular function by transthoracic echocardiography, VICORDER and USCOM. SPSS was used for statistical analysis.

RESULTS: 53 participants after PE/FGR (13 with former PETN intake) and 51 controls were recruited for follow-up at an average of 14 years after index pregnancies. Compared to controls, women after PE/FGR had a significantly higher incidence of arterial hypertension (13.7% vs. 41.5%, p<0.001), and were more likely to be hypertensive (41.2% vs. 67.30%, p = 0.008). There were no differences in cardiovascular function observed. Affected women with PETN intake during pregnancy showed lower mean values for right atrial area and ventricle in comparison to controls and also to affected women without former medication.

CONCLUSIONS: In conclusion, our study results confirm that the risk of CVD is increased in women after PE/FGR compared to women after uneventful pregnancies. Contrary to our expectations, no major cardiovascular changes were observed in our cohort 10-20 years post pregnancy. The observed differences found in right heart dimensions were within reference ranges, and should be interpreted with caution.

PMID:39405308 | DOI:10.1371/journal.pone.0309177

PLoS One. 2024 Oct 15;19(10):e0311542. doi: 10.1371/journal.pone.0311542. eCollection 2024.

ABSTRACT

The aim of this research is to investigate the role of servicescape on re-patronage and recommended intention through pleasure feeling and satisfaction in healthcare settings that put substantial contribution in the process of healthcare service delivery. Data were collected through cross-sectional convenience sampling via a self-administered survey questionnaire from 431 clinical outpatients who revisit the same hospital of metropolitan areas of Punjab, Pakistan. Structural Equation Modeling (SEM) was carried out for path analysis through AMOS (24.0 V), while statistical measures were analyzed using SPSS (25.0 V). The present study results revealed that patients’ intention optimistically triggered through partial mediation and affirm the direct and indirect association with servicescape. It also revealed that patient-recommended and re-patronage intentions to visit the clinic were statistically substantial and positively influenced by intervening constructs of pleasure feeling and satisfaction. Additionally, it is found that servicescape and pleasure feeling contributed to 30% change in satisfaction. Moreover, pleasure feeling, and satisfaction contributed to 50% change in re-patronage and 31% change in recommendation intention of the patients. The current study findings contribute significantly to servicescape literature from a theatrical perspective and reevaluate the patterns and operations in healthcare. It also helps managers and administrators of private hospitals to make strategies to increase patient satisfaction.

PMID:39405306 | DOI:10.1371/journal.pone.0311542

J Med Internet Res. 2024 Oct 15;26:e49704. doi: 10.2196/49704.

ABSTRACT

BACKGROUND: Studies have shown that patients have difficulty understanding medical jargon in electronic health record (EHR) notes, particularly patients with low health literacy. In creating the NoteAid dictionary of medical jargon for patients, a panel of medical experts selected terms they perceived as needing definitions for patients.

OBJECTIVE: This study aims to determine whether experts and laypeople agree on what constitutes medical jargon.

METHODS: Using an observational study design, we compared the ability of medical experts and laypeople to identify medical jargon in EHR notes. The laypeople were recruited from Amazon Mechanical Turk. Participants were shown 20 sentences from EHR notes, which contained 325 potential jargon terms as identified by the medical experts. We collected demographic information about the laypeople’s age, sex, race or ethnicity, education, native language, and health literacy. Health literacy was measured with the Single Item Literacy Screener. Our evaluation metrics were the proportion of terms rated as jargon, sensitivity, specificity, Fleiss κ for agreement among medical experts and among laypeople, and the Kendall rank correlation statistic between the medical experts and laypeople. We performed subgroup analyses by layperson characteristics. We fit a beta regression model with a logit link to examine the association between layperson characteristics and whether a term was classified as jargon.

RESULTS: The average proportion of terms identified as jargon by the medical experts was 59% (1150/1950, 95% CI 56.1%-61.8%), and the average proportion of terms identified as jargon by the laypeople overall was 25.6% (22,480/87,750, 95% CI 25%-26.2%). There was good agreement among medical experts (Fleiss κ=0.781, 95% CI 0.753-0.809) and fair agreement among laypeople (Fleiss κ=0.590, 95% CI 0.589-0.591). The beta regression model had a pseudo-R² of 0.071, indicating that demographic characteristics explained very little of the variability in the proportion of terms identified as jargon by laypeople. Using laypeople’s identification of jargon as the gold standard, the medical experts had high sensitivity (91.7%, 95% CI 90.1%-93.3%) and specificity (88.2%, 95% CI 86%-90.5%) in identifying jargon terms.

CONCLUSIONS: To ensure coverage of possible jargon terms, the medical experts were loose in selecting terms for inclusion. Fair agreement among laypersons shows that this is needed, as there is a variety of opinions among laypersons about what is considered jargon. We showed that medical experts could accurately identify jargon terms for annotation that would be useful for laypeople.

PMID:39405109 | DOI:10.2196/49704