Management Decision-making Research Articles

A4 Thoracolumbar Fracture Class Is Associated With a Greater Degree of Vertebral Height Loss in Conservatively Managed Patients.

Thoracolumbar (TL) junction fractures are common, often resulting from high-energy trauma or osteoporosis, and may lead to neurological deficits, deformities, or chronic pain. Treatment decisions for neurologically intact patients remain controversial, with nonsurgical management often favored. The AO classification system has been used to characterize thoracolumbar fractures using fracture morphology and clinical factors affecting clinical decision-making for fracture management. This study aims to assess the radiographic outcomes of utilizing a thoracolumbosacral orthosis (TLSO) brace in neurologically intact patients with TL fractures based on the AO classification system. A retrospective analysis of 43 patients was conducted using data from the VCU Spine Database on patients with TL fractures managed conservatively with a TLSO brace from 2010 to 2019. Demographic variables and radiographic measurements of anterior height loss were analyzed and stratified by AO fracture class. Significant differences were observed in anterior height loss between AO fracture classes, with A4 fractures showing significantly greater anterior height loss at initial presentation (27.6 + 4.8%) compared to A1/A2 (16.1 + 2.2%; p=0.049). At follow up, A4 fractures had a significantly greater anterior height loss (40.2 + 6.6%) than both the A1/A2 (22.4 + 2.9%; p=0.029) and A3 fracture classes (20.5 + 3.6; p=0.020). The study highlights significant differences in anterior height loss among AO fracture classes, suggesting varying degrees of severity and potential implications for clinical management. While conservative treatment with TLSO braces may provide pain relief, surgical intervention may offer better structural recovery, especially in more severe fractures. Conservative management of TL fractures with TLSO braces may result in greater anterior height loss, particularly in A4 fractures, emphasizing the need for individualized treatment decisions. Further research, including prospective studies, is warranted to validate these findings and guide clinical practice effectively.

Place meanings of shore recreational fishers in the urban fringe

AbstractWhile recreational fishers recognize the many benefits of their practice, there is less recognition of these benefits by decision-makers in natural resource management. Fisheries motivations and benefits have not been sufficiently captured in urban and urban/suburban interface (urban fringe) areas. To help address this gap, this work captures fisheries motivations and benefits in the urban fringe of Rhode Island using a place meaning methodology. In-person interviews were conducted in Spanish and English with anglers who use public access sites in the urban fringe near Providence, Rhode Island. This area was found to support diverse users with needs for access, suitable water quality, and safety. Mental and physical health benefits were also found. The results inform fisheries management and landscape planning via a greater understanding of the conceptualization of coastal spaces and suggestions for future attributes of coastal spaces. Future directions to explore include the relationship between place meanings and well-being, the relationship between place meanings and fishing motivations, and gathering place meanings across diverse users and locations. Understanding recreational fishing motivations and benefits in the urban fringe informs fisheries and coastal management decision-making under potential landscape changes or fishery stock decline.

Large Language Model Influence on Management Reasoning: A Randomized Controlled Trial.

Large language model (LLM) artificial intelligence (AI) systems have shown promise in diagnostic reasoning, but their utility in management reasoning with no clear right answers is unknown. To determine whether LLM assistance improves physician performance on open-ended management reasoning tasks compared to conventional resources. Prospective, randomized controlled trial conducted from 30 November 2023 to 21 April 2024. Multi-institutional study from Stanford University, Beth Israel Deaconess Medical Center, and the University of Virginia involving physicians from across the United States. 92 practicing attending physicians and residents with training in internal medicine, family medicine, or emergency medicine. Five expert-developed clinical case vignettes were presented with multiple open-ended management questions and scoring rubrics created through a Delphi process. Physicians were randomized to use either GPT-4 via ChatGPT Plus in addition to conventional resources (e.g., UpToDate, Google), or conventional resources alone. The primary outcome was difference in total score between groups on expert-developed scoring rubrics. Secondary outcomes included domain-specific scores and time spent per case. Physicians using the LLM scored higher compared to those using conventional resources (mean difference 6.5 %, 95% CI 2.7-10.2, p<0.001). Significant improvements were seen in management decisions (6.1%, 95% CI 2.5-9.7, p=0.001), diagnostic decisions (12.1%, 95% CI 3.1-21.0, p=0.009), and case-specific (6.2%, 95% CI 2.4-9.9, p=0.002) domains. GPT-4 users spent more time per case (mean difference 119.3 seconds, 95% CI 17.4-221.2, p=0.02). There was no significant difference between GPT-4-augmented physicians and GPT-4 alone (-0.9%, 95% CI -9.0 to 7.2, p=0.8). LLM assistance improved physician management reasoning compared to conventional resources, with particular gains in contextual and patient-specific decision-making. These findings indicate that LLMs can augment management decision-making in complex cases. ClinicalTrials.gov Identifier: NCT06208423; https://classic.clinicaltrials.gov/ct2/show/NCT06208423.

PBPK model-based gender-specific risk assessment of N-nitrosodimethylamine (NDMA) using human biomonitoring data.

Despite several screening levels for NDMA reported in water, soil, air, and drugs, the human risk assessment using biomonitoring concentrations has not been performed. In this study, gender-specific exposure guidance values were determined in humans, then biomonitoring measurements in healthy Korean subjects (32 men and 40 women) were compared to the exposure guidance values to evaluate the current exposure level to NDMA. For the human risk assessment of NDMA, the gender-specific physiologically based pharmacokinetic (PBPK) model was developed in humans using proper physiological parameters, partition coefficients, and biochemical parameters. Using the PBPK model, a Monte Carlo simulation was performed to describe the magnitudes of inter-individual variability and uncertainty on the single model predictions. The PBPK modeling and Monte Carlo simulation allowed the estimation of the relationship between external dose and blood concentration for the risk assessment. The procedure for the human risk assessment was summarized as follows: (1) estimating a steady-state blood concentration (Cavg) corresponding to the daily no observed adverse effect level (NOAEL) administration in rats; (2) applying uncertainty factors (UFs) for deriving the human Cavg; (3) determining the exposure guidance values as screening criteria; (4) interpreting the human biomonitoring measurements by forward and reverse dosimetry approaches. Using the biomonitoring concentrations, current daily exposures to NDMA were estimated to be 3.95μg/day/kg for men and 10.60μg/day/kg for women, respectively. The result of the study could be used as a basis for implementing further risk management and regulatory decision-making for NDMA.

The increase of an allelopathic and unpalatable plant undermines reindeer pasture quality and current management in the Norwegian tundra

Ongoing Arctic greening can increase productivity and reindeer pasture quality in the tundra. However, greening may also entail proliferation of unpalatable species, with consequences for pastoral social-ecological systems. Here we show extensive greening across 20 reindeer districts in Norway between 2003 and 2020, which has reduced pasture diversity. The allelopathic, evergreen dwarf-shrub crowberry increased its biomass by 60%, with smaller increases of deciduous shrubs and no increase in forbs and graminoids, the most species rich growth forms. There was no evidence for higher reindeer densities promoting crowberry. The current management decision-making process aims at sustainable pasture management but does not explicitly account for pasture changes and reduced diversity. Large-scale shifts towards evergreening and increased allelopathy may thus undermine the resource base for this key Arctic herbivore and the pastoral social-ecological system. Management that is sensitive to changes in pasture diversity could avoid mismanagement of a social-ecological system in transition.

Advances in Understanding the Toxicity of 4-Hydroxyphenylpyruvate Dioxygenase-Inhibiting Herbicides.

4-Hydroxyphenylpyruvate dioxygenase inhibiting herbicides (HIHs) represent a recent class (HRAC group 27) of herbicides that offer many advantages, such as broad-spectrum activity, crop selectivity, and low resistance rates. However, emerging studies have highlighted the potential toxicity of HIHs in the environment. This review aims to provide a comprehensive summary of the toxicity of HIHs toward nontarget organisms, including plants, microorganisms, animals, and humans. Furthermore, the present work discusses the ecological roles of these organisms in the environment and their significance in agriculture. By shedding light on the toxicity of HIHs, this study seeks to raise awareness among end users, including environmentalists, researchers, and farmers, regarding the potential ecological implications of these herbicides. Hopefully, this knowledge can contribute to informed decision-making and sustainable practices in green agriculture and environmental management.

Reactions to Algorithmic Decision-Making in Human Resource Management: A Meta-Analysis

Reactions to Algorithmic Decision-Making in Human Resource Management: A Meta-Analysis

Occupational stress and associated sources and risk factors among nurses in Gaza strip, Palestine: A cross-sectional survey.

To determine the occurrence of occupational stress among Palestinian nurses, and their associated sources and risk factors. A cross-sectional descriptive design. A total of 250 registered nurses from eight governmental hospitals, using a convenience sampling method. Data collection were conducted using the 30-items self-reported Occupational Stress Scale from December 2022 to March 2023. Descriptive statistics, independent sample t-test, one-way ANOVA, and multiple linear regression analysis were applied to analysis data. Data analysis included descriptive statistics, independent sample t-test, one-way ANOVA, and multiple linear regression analysis. The prevalence of high occupational stress levels was 64.8% (Mean = 3.9 out of 5). The main sources of stress are too much responsibility and work, understaffing, lack of promotion and recognition, inadequate pay, time pressure, and management style. The results regression analysis demonstrated that male nurses with a Masters or PhD degree and those working in fixed shifts experienced higher occupational stress. Moreover, participants who worked overtime hours were more susceptible to stress. The research indicates that occupational stress presents a notable challenge for nurses in the Gaza Strip, Palestine. It suggests that in order to alleviate this stress, decision-makers in healthcare policy and hospital management should prioritize the execution of strategies aimed at addressing the primary stressors and risk factors identified. This study adhered to the STROBE guidelines. A total of 250 registered nurses were taken part in this study by answering a self-administered study survey.

Evaluation and interpretation of landscapes from satellite imagery

Satellite imagery allows us to view landscapes from a bird’s eye view, providing a new dimension in appreciating the environments we inhabit. This alternative perspective has the potential to shape individual perceptions of landscapes and play a pivotal role in land management decision-making and communication. However, the interpretation and appreciation of landscapes seen in satellite imagery may vary among observers. This study investigates the relationship between individuals’ ability to interpret images from eye-level and satellite perspectives, their familiarity with the landscape, and their appreciation of land cover from this viewpoint. To achieve this, a survey was conducted presenting respondents with images of land cover classes captured at eye level and from satellite imagery of the Yungay municipality in Chile. Participants were asked to interpret the primary land use land cover (LULC) depicted in the imagery and indicate their appreciation of that landscape. Variation in the interpretation of LULC was observed between the image source and land cover type. For instance, forest classes seen in eye-level imagery were more accurately interpreted compared to satellite imagery, while the reverse was true for agriculture. These differences in interpretation also impacted the appreciation scores assigned to the landscapes in the images. Specifically, if respondents perceived an image to be dominated by a traditionally appreciated land cover (e.g., Native Vegetation), they provided a higher score, even if the image depicted another class (e.g., Plantation Forestry). These findings highlight that considering the influence of satellite imagery in shaping perception is crucial in supporting land management activities.

Integrating mechanistic-based and classification-based concepts into perioperative pain management: an educational guide for acute pain physicians

Chronic pain begins with acute pain. Physicians tend to classify pain by duration (acute vs chronic) and mechanism (nociceptive, neuropathic and nociplastic). Although this taxonomy may facilitate diagnosis and documentation,...

Using an interprofessional decision-making model to determine the level of collaborative decision-making in a virtual learning environment

Medicine and pharmacy students benefit from interprofessional education that supports the learning of collaboration skills for medicines management decision-making. However, if and how students undertake collaborative decision-making processes in interprofessional student teams has not been investigated. The aim of this study was to determine the level to which pharmacy and medical students collaborate to make medicines management decisions in a learning activity. Eight pharmacy and 13 medical students completed a 9-item survey to evaluate a interprofessional learning activity. A secondary analysis of survey data using a deductive approach, according to the Model of Interprofessional Decision-Making was undertaken. Different levels of collaboration were identified in the students’ responses ranging from the first and lowest level in the model, Individual Decision Making, to the fourth and highest level, Shared Decision. Most student responses were categorised as level three-Deliberation. Findings from this study indicate that pharmacy and medical students collaborate to different levels with each other to make patient care decisions. Further examination is required to see if this is usual variation or related to the design of the learning activity.

Assessment of ecosystem services of recreational and health-improving forests in Ivano-Frankivsk Region

Optimising the interaction between society and the environment is one of the fundamental directions of recreational forest management. To improve the efficiency of using ecosystem services of recreational and health-improving forests, there is a need to develop new approaches and tools for management decision-making, a key component of which is the assessment of these beneficial goods. The aim of the study is to analyse public opinion regarding the expressed and identified preferences for the characteristics of recreational forests and recreational facilities, as well as to assess the willingness to pay for preferred forest characteristics and the maintenance of recreational facilities in proper condition. The study was conducted from June to August 2022 using a direct and anonymous online survey. It was found that respondents are willing to pay an average of 1-100 UAH/year to create and maintain the preferred characteristics of the forest and 101-500 UAH/year to create and maintain recreational facilities. As for the respondents’ perception of forest characteristics that affect the choice of a place to relax, preference is given to mixed forests (74.8%), in which three tree species grow (57.9%), and forests up to 20 meters high (40.2%). On the other hand, only 15.9% of respondents preferred forests taller than 20 meters. The presence of deadwood in the forest is positively perceived by 51.4% of respondents, and 10.3% do not pay attention to the presence or absence of standing dead and drying up trees in the forest. The most important needs for which respondents visit recreational forests were walks, family recreation, and observing forest landscapes. The integration of the identified perceptions and preferences regarding recreational forests and objects into innovative decision-making in the field of forestry ensures a synergy of benefits, such as increasing the attractiveness of recreational forests and forest landscapes, increasing the resilience of forests to climate change, and meeting the local demand for wood biomass

Associations between patient portal use and electronic health record (EHR) data timeliness in type 2 diabetes mellitus care

Abstract Objective Patient data is subject to missingness and errors. Patient portals enable patients managing type 2 diabetes mellitus (T2DM) to review and correct data to avoid retesting, medication errors, and diagnostic mistakes. We examined whether patient portal use was associated with electronic health record (EHR) data timeliness in T2DM care. Research Design and methods We analyzed EHR data from a panel of adult patients to determine whether portal use improved data timeliness. EHR data timeliness is measured as the number of days between patient encounters, accounting for mean attribute update periods, where available EHR attribute updates for T2DM measurements were present, including body mass, weight, glycated hemoglobin A1c, cholesterol, blood pressure, serum creatinine, and smoking status. We performed negative binomial regressions with fixed effects to estimate the association between patient portal use and EHR data timeliness. Sensitivity analyses were conducted using Poisson regressions. Results Nearly a third (31.3%) of patients in our sample actively used the health portal. There were fewer days (111.9 days vs. 136.7 days; p &lt; 0.001) between EHR attribute updates for patients who used health portals compared to patients who did not. Data timeliness was lower among female, non-Hispanic White Medicare beneficiaries. Based on regression analyses, portal use was associated with an expected 3.6 (p &lt; 0.001) percentage point decrease in days between attribute updates, indicating improved EHR timeliness. Conclusion Improving the quality of health information may streamline decision-making in partnership with patients who produce data points across clinical settings. Active use of patient portals and digital health tools in chronic disease care are critical for care management and clinical decision-making, especially for patients managing type 2 diabetes across clinical settings.

Identifying manning roughness coefficient using automatic calibration method and simulation of pollution incidents in the Nile River, Egypt

Study regionA reach of the Nile River located between Naga Hammadi barrage and Asyut barrage, Egypt Study focusAn accurate representation of hydrodynamics of an important water source helps cope with expected future climate changes, pollution incidents and water quality problems. Here, a comparison between HEC-RAS 1D and TELEMAC-2D model was conducted by identifying different Manning coefficients. Moreover, an automatic calibration using Dual-Annealing optimization method was applied for first time to calibrate the model with non-uniform Manning coefficients. The transport of tracer (pollution) was simulated by computing tracer residence times. Pollution transport scenarios were discussed to draw a picture of pollution incidents which will continue to happen in the future. An equation indicating the relation between flow discharge and residence time was derived to hurriedly help decision makers in water management during sudden pollution incidents. New hydrological insights for the regionA model with spatially variable Manning coefficients using TELEMAC-2D was set up and calibrated achieving good accuracyies with average errors of approximately 4 cm and 7 cm between field and simulated water levels for two different discharge scenarios. Moreover, an equation for relation between flow discharge and residence time was derived producing a strong correlation coefficient of 0.95. This study, integrating advanced hydrodynamic models and automatic calibration techniques, provides a robust framework for assessing and managing water resource challenges under varying flow conditions.

Implementation of Long Short-Term Memory Network for Predicting The Cocoa Crop Yield

Forecasting models with high accuracy become more important during uncertain conditions, such as climate change, that could have a high effect. The forecast model's accuracy in predicting cocoa crop yield must be high to determine decision-making in management. Seven different potential predictor variables have been analyzed in this research to see the influence of cocoa crop yield. Using a scatter plot diagram, six of seven variables, relative humidity, maximum temperature, minimum temperature, evapotranspiration, rainfall, and soil moisture, are proven to influence cocoa crop yield. Then, those datasets are divided into training and validation sets using multiple linear regression analysis and a Long Short-Term Memory (LSTM) network. The output model of those methods is assessed using two metrics: coefficient of determination and Root Means Square Error (RMSE). From those model performance metrics, LSTM outperformed multiple linear regression analysis. LSTM has an R-square of 98% and an RMSE of 0.3 while multiple linear regression just reached 82% of the R-square and 2.57 of the RMSE. The LSTM model has been proven to be valid.

Application of Shannon Entropy in Assessing Changes in Precipitation Conditions and Temperature Based on Long-Term Sequences Using the Bootstrap Method

This study delves into the application of Shannon entropy to analyze the long-term variability in climate data, specifically focusing on precipitation and temperature. By employing data from 1901 to 2010 across 377 catchments worldwide, we investigated the dynamics of climate variables using the generalized extreme value (GEV) distribution and Shannon entropy measures. The methodology hinged on the robust bootstrap technique to accommodate the inherent uncertainties in climatic data, enhancing the reliability of our entropy estimates. Our analysis revealed significant trends in entropy values, suggesting variations in the unpredictability and complexity of climate behavior over the past century. These trends were critically assessed using non-parametric tests to discern the underlying patterns and potential shifts in climate extremes. The results underscore the profound implications of entropy trends in understanding climate variability and aiding the prediction of future climatic conditions. This research not only confirms the utility of Shannon entropy in climatological studies but also highlights its potential in enhancing our understanding of complex and chaotic climate systems. The study’s findings are vital for developing adaptive strategies in response to the evolving nature of climate extremes, thus contributing to more informed decision-making in environmental management and policy formulation.

Evolution Characteristics of Meteorological and Hydrological Drought in an Arid Oasis of Northwest China

In the context of global warming, the acceleration of the water cycle increases the risk of meteorological drought (MD) and hydrological drought (HD) in the arid region of Northwest China. The Manas River Basin is a typical agricultural oasis and the largest oasis farming area in Xinjiang, Northwest China. Droughts in this basin have significant implications for both agricultural production and the livelihoods of inhabitants. To evaluate the MD and HD and provide information for drought relief in the MRB, the standardized precipitation evapotranspiration index (SPEI) and standardized runoff index (SRI) were calculated using long-term rainfall and runoff data. Subsequently, combined with ArcGIS 10.3 software and the trend analysis method, the SPEI and SRI characteristics were evaluated at different time scales (1-, 3-, 6-, and 12-month). There were three main findings. First, both MD and HD were alleviated, with significantly more HD alleviation. MDs in spring and autumn exhibited a trend of aggravation. The SRIs in summer, autumn, and winter increased significantly at a confidence level of p &lt; 0.01, with an insignificant decline in spring. In the 2010s, the frequency of light drought of MD was stable at 10% to 20%, while severe and extreme droughts increased. The frequency of HDs has decreased since the 1990s. Second, on annual and seasonal scales, MDs occurred mainly as light and moderate droughts. The highest frequency of MD was 24% of moderate droughts in winter. Spatially, the northern region of the MRB was characterized by more frequent light and extreme droughts. Third, runoff in the Manas River Basin increased significantly during the 1990s, which may have been related to the acceleration of glacial retreat in the Tianshan Mountains. This study can effectively reveal the changes in meteorological and hydrological drought in NWC and provide the basis for risk decision-making and management for watershed managers.

BIG DATA-DRIVEN DECISION MAKING IN PROJECT MANAGEMENT: A COMPARATIVE ANALYSIS

This study investigates the impact of big data-driven decision-making in construction project management through a qualitative comparative analysis. By conducting semi-structured interviews with project managers, data analysts, and construction workers across various types of construction projects, the research identifies key themes related to the benefits and challenges of integrating big data analytics. The findings highlight significant advantages such as enhanced operational efficiency, improved decision-making processes, cost reduction, budget management, timely project delivery, and quality control and assurance. However, challenges including data integration complexities, privacy concerns, the need for specialized skills, and organizational resistance to change are also revealed. The study underscores the importance of fostering a data-driven culture and strong leadership support to maximize the benefits of big data in construction project management, while also emphasizing the need for context-specific strategies tailored to different project types.

Predictive Insight into Tailings Flowability at Their Disposal Using Operating Data-Driven Artificial Neural Network (ANN) Technique

This study investigates the application of artificial neural networks (ANNs) in predicting the flowability of mining tailings based on operational variables. As the mining industry seeks to enhance operations with complex ores, the constant improvement and optimization of mineral waste management are crucial. The flowability of tailings was investigated with data driven by properties such as particle-size distribution, water content, compaction capacity, and viscoelastic characteristics that can directly affect stacking, water recovery capabilities, and stability at disposal, influencing storage capacity, operational continuity, and work safety. There was a strong correlation between water content and tailings flowability, emphasising its importance in operational transport and deposition. Three ANN models were evaluated to predict tailings flowability across three and five categories, where a model based on thickening operational variables, including yield stress and turbidity, demonstrated the highest accuracy, achieving up to 94.4% in three categories and 88.9% in five categories. Key variables such as flocculant dosage, water content, yield stress, and solid concentration were identified as crucial for prediction accuracy The findings suggest that ANN models, even with limited datasets, can provide reliable flowability predictions, supporting tailings management and operational decision-making.

Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region

Accurately estimating aboveground biomass (AGB) in forest ecosystems facilitates efficient resource management, carbon accounting, and conservation efforts. This study examines the relationship between predictors from Landsat-9 remote sensing data and several topographical features. While Landsat-9 provides reliable data crucial for long-term monitoring, it is part of a broader suite of available remote sensing technologies. We employ machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Random Forest (RF), alongside linear regression techniques like Multiple Linear Regression (MLR). The primary objectives of this study encompass two key aspects. Firstly, the research methodically selects optimal predictor combinations from four distinct variable groups: Landsat-9 (L1) data, a fusion of Landsat-9 data and Vegetation-based indices (L2), and the integration of Landsat-9 data with the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) (L3) and the combination of best predictors (L4) derived from L1, L2, and L3. Secondly, the research systematically assesses the effectiveness of different algorithms to identify the most precise method for establishing any potential relationship between field-measured AGB and predictor variables. Our study revealed that the Random Forest (RF) model was the most efficient method utilizing Landsat-9 OLI and SRTM DEM (L3) predictors, achieving remarkable accuracy. This conclusion was reached by assessing its outstanding performance when compared to an independent validation dataset. The RF model exhibited remarkable accuracy, presenting relative mean absolute error (RMAE), relative root mean square error (RRMSE), and R2 values of 14.33%, 22.23%, and 0.81, respectively. The XGBoost model is the subsequent choice with RMAE, RRMSE, and R2 values of 15.54%, 23.85%, and 0.77, respectively. The study further highlights the significance of specific spectral bands, notably B4 and B5 from Landsat 9 OLI data, in capturing spatial AGB distribution patterns. Integration of vegetation-based indices, including TNDVI, NDVI, RVI, and GNDVI, further refines AGB mapping precision. Elevation, slope, and the Topographic Wetness Index (TWI) are crucial proxies for representing biophysical and biological mechanisms impacting AGB. Through the utilization of openly accessible fine-resolution data and employing the RF algorithm, the research demonstrated promising outcomes in the identification of optimal predictor-algorithm combinations for forest AGB mapping. This comprehensive approach offers a valuable avenue for informed decision-making in forest management, carbon assessment, and ecological monitoring initiatives.