Prioritization Of Resources Research Articles

External Validation of the Predictive Accuracy of Clinical and Immunological Scores in COVID-19 Outcomes in a Retrospective Cohort Study

Background and Objectives: The COVID-19 pandemic has necessitated the development of reliable prognostic tools to predict patient outcomes and guide clinical decisions. This study evaluates the predictive utility of several clinical scores—PAINT, ISARIC4C, CHIS, COVID-GRAM, SOFA, and CURB-65—for in-hospital mortality among COVID-19 patients, comparing their effectiveness at admission and seven days post-symptom onset. Methods: In this retrospective cohort study conducted at the Clinical Emergency Hospital Pius Brînzeu in Timișoara, adult patients hospitalized with confirmed SARS-CoV-2 infection were included. The study was approved by the Local Ethics Committee, adhering to GDPR and other regulatory standards. Prognostic scores were calculated using patient data at admission and Day 7. Statistical analyses included ROC curves, Kaplan–Meier survival analysis, and multivariate Cox regression. Results: The study comprised 269 patients, with a notable distinction in outcomes between survivors and non-survivors. Non-survivors were older (mean age 62.12 years) and exhibited higher comorbidity rates, such as diabetes (55.56% vs. 31.06%) and cardiovascular diseases (48.15% vs. 29.81%). Prognostic scores were significantly higher among non-survivors at both time points, with PAINT and ISARIC4C showing particularly strong predictive performances. The AUROC for PAINT increased from 0.759 at admission to 0.811 by Day 7, while ISARIC4C demonstrated an AUROC of 0.776 at admission and 0.798 by Day 7. Multivariate Cox regression indicated that a PAINT score above 8.10 by Day 7 was associated with a hazard ratio (HR) of 4.9 (95% CI: 3.12–7.72) for mortality. Conclusions: The study confirms the strong predictive value of the PAINT, ISARIC4C, CHIS, COVID-GRAM, SOFA, and CURB-65 scores in determining mortality risk among hospitalized COVID-19 patients. These scores can significantly aid clinicians in early-risk stratification and resource prioritization, potentially enhancing patient management and outcomes in acute care settings.

Creating a culture of assessment in student affairs to inform practices, priorities, and fiscal decisions utilizing the CAS standards framework at small institutions

AbstractDoing more with less is a continual dynamic faced by leaders in higher education, particularly within small institutions where resources can be scarce, and professionals often are wearing multiple hats. The ability to prioritize programs and services in student affairs rises to the forefront of our work every day. As a result, leaders often need to make strategic decisions regarding what to continue, what to sunset, and how to invest in new innovations to promote student success. This article will focus on strategies for student affairs professionals at small institutions to make and implement decisions regarding our work by building a culture of assessment within student affairs through the utilization of the Council for Advancement of Standards (CAS) standards to inform the strategic prioritization of limited resources.

Spatiotemporal Cluster Detection for COVID-19 Outbreak Surveillance: Descriptive Analysis Study.

During the peak of the winter 2020-2021 surge, the number of weekly reported COVID-19 outbreaks in Washington State was 231; the majority occurred in high-priority settings such as workplaces, community settings, and schools. The Washington State Department of Health used automated address matching to identify clusters at health care facilities. No other systematic, statewide outbreak detection methods were in place. This was a gap given the high volume of cases, which delayed investigations and decreased data completeness, potentially leading to undetected outbreaks. We initiated statewide cluster detection using SaTScan, implementing a space-time permutation model to identify COVID-19 clusters for investigation. To improve outbreak detection, the Washington State Department of Health initiated a systematic cluster detection model to identify timely and actionable COVID-19 clusters for local health jurisdiction (LHJ) investigation and resource prioritization. This report details the model's implementation and the assessment of the tool's effectiveness. In total, 6 LHJs participated in a pilot to test model parameters including analysis type, geographic aggregation, cluster radius, and data lag. Parameters were determined through heuristic criteria to detect clusters early when they are smaller, making interventions more feasible. This study reviews all clusters detected after statewide implementation from July 17 to December 17, 2021. The clusters were analyzed by LHJ population and disease incidence. Clusters were compared with reported outbreaks. A weekly, LHJ-specific retrospective space-time permutation model identified 2874 new clusters during this period. While the weekly analysis included case data from the prior 3 weeks, 58.25% (n=1674) of all clusters identified were timely-having occurred within 1 week of the analysis and early enough for intervention to prevent further transmission. There were 2874 reported outbreaks during this same period. Of those, 363 (12.63%) matched to at least one SaTScan cluster. The most frequent settings among reported and matched outbreaks were schools and youth programs (n=825, 28.71% and n=108, 29.8%), workplaces (n=617, 21.46% and n=56, 15%), and long-term care facilities (n=541, 18.82% and n=99, 27.3%). Settings with the highest percentage of clusters that matched outbreaks were community settings (16/72, 22%) and congregate housing (44/212, 20.8%). The model identified approximately one-third (119/363, 32.8%) of matched outbreaks before cases were associated with the outbreak event in our surveillance system. Our goal was to routinely and systematically identify timely and actionable COVID-19 clusters statewide. Regardless of population or incidence, the model identified reasonably sized, timely clusters statewide, meeting the objective. Among some high-priority settings subject to public health interventions throughout the pandemic, such as schools and community settings, the model identified clusters that were matched to reported outbreaks. In workplaces, another high-priority setting, results suggest the model might be able to identify outbreaks sooner than existing outbreak detection methods.

ENHANCING THE RESILIENCE OF IOT NETWORKS: STRATEGIES AND MEASURES FOR MITIGATING DDOS ATTACKS

The Internet of Things (IoTs) are emerging and become a vital need in our daily routine. The privacy protection and insecurity of these IoT-based devices face many challenges. Distributed denial of service (DDoS) attacks in IoT networks become a significant growing challenge that is addressed in this research. The resilience and strategy for IoT devices due to distributed denial of service (DDoS) attacks assess current security measures by proposing modern procedures to upgrade the strength of IoT frameworks. This article proposes a mechanism that mitigiates the effects of DDoS attacks in IoTs, that cause significant destruction to existing systems. Utilizing secondary data from Kaggle, the machine is trained and tested. Our proposed approach incorporates descriptive statistics, correlations, t-tests, chi-square tests, and regression analyses to supply a systematic understanding of IoT security by critically analyzing the existing variants of numerous DDoS attacks, Security issues in IoTs, and creation of them in Botnets or zombies. Our findings show that the proposed security techniques are viable and detection rates correlate with security viability. The proposed model asses various network threat and cybersecurity arrangements for mitigating DDoS attacks in IoT’s and outperforms the previously implemented Web Application Firewall (WAF), Bot Mitigation, Resource Prioritisation, and Content Delivery Networks (CDNs)based DDoS mitigation techniques by 80.5%, 88%, 86% in terms of effectiveness, T-test, chi test, and correlation.

Economic Burden of Community-Acquired Antibiotic-Resistant Urinary Tract Infections: Systematic Review and Meta-Analysis.

Antibiotic resistance (ABR) poses a major burden to global health and economic systems. ABR in community-acquired urinary tract infections (CA-UTIs) has become increasingly prevalent. Accurate estimates of ABR's clinical and economic burden are needed to support medical resource prioritization and cost-effectiveness evaluations of urinary tract infection (UTI) interventions. This study aims to systematically synthesize the evidence on the economic costs associated with ABR in CA-UTIs, using published studies comparing the costs of antibiotic-susceptible and antibiotic-resistant cases. We searched the PubMed, Ovid MEDLINE and Embase, Cochrane Review Library, and Scopus databases. Studies published in English from January 1, 2008, to January 31, 2023, reporting the economic costs of ABR in CA-UTI of any microbe were included. Independent screening of titles/abstracts and full texts was performed based on prespecified criteria. A quality assessment was performed using the Integrated Quality Criteria for Review of Multiple Study Designs (ICROMS) tool. Data in UTI diagnosis criteria, patient characteristics, perspectives, resource costs, and patient and health economic outcomes, including mortality, hospital length of stay (LOS), and costs, were extracted and analyzed. Monetary costs were converted into 2023 US dollars. This review included 15 studies with a total of 57,251 CA-UTI cases. All studies were from high- or upper-middle-income countries. A total of 14 (93%) studies took a health system perspective, 13 (87%) focused on hospitalized patients, and 14 (93%) reported UTI pathogens. Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa are the most prevalent organisms. A total of 12 (80%) studies reported mortality, of which, 7 reported increased mortality in the ABR group. Random effects meta-analyses estimated an odds ratio of 1.50 (95% CI 1.29-1.74) in the ABR CA-UTI cases. All 13 hospital-based studies reported LOS, of which, 11 reported significantly higher LOS in the ABR group. The meta-analysis of the reported median LOS estimated a pooled excess LOS ranging from 1.50 days (95% CI 0.71-4.00) to 2.00 days (95% CI 0.85-3.15). The meta-analysis of the reported mean LOS estimated a pooled excess LOS of 2.45 days (95% CI 0.51-4.39). A total of 8 (53%) studies reported costs in monetary terms-none discounted the costs. All 8 studies reported higher medical costs spent treating patients with ABR CA-UTI in hospitals. The highest excess cost was observed in UTIs caused by carbapenem-resistant Enterobacterales. No meta-analysis was performed for monetary costs due to heterogeneity. ABR was attributed to increased mortality, hospital LOS, and economic costs among patients with CA-UTI. The findings of this review highlighted the scarcity of research in this area, particularly in patient morbidity and chronic sequelae and costs incurred in community health care. Future research calls for a cost-of-illness analysis of infections, standardizing therapy-pathogen combination comparators, medical resources, productivity loss, intangible costs to be captured, and data from community sectors and low-resource settings and countries.

“You walk on glass if you are in that space”: Risks and harms of corruption in wildlife justice pathways in Uganda

Wildlife crime drives existential harms including biodiversity loss and climate change, disproportionately affecting the Global South. Criminal justice approaches dominate the public response, yet scant evidence exists on how corruption, an economic crime, undermines deterrence and exacerbates inequalities. This primary research study utilises a qualitative approach to examine the risks and harms of corruption within wildlife enforcement and justice in Uganda. Using a red-green criminological perspective and drawing upon semi-structured interviews with experts and practitioners from government and civil society, this paper identifies corruption risks in wildlife criminal justice and explores their related harms and consequences. The study finds wildlife enforcement in Uganda suffers from institutional corruption, including perverse incentives which distort targeting and resource prioritisation, leading to discrimination against low-level offenders. Furthermore, differential access to individualist corruption opportunities along the justice pathway enables wealthy or well-connected suspects to avoid sanctions, resulting in sentencing inequities. Despite resulting in differential outcomes for affluent and subsistence offenders, the study concludes that corruption in wildlife justice neutralises deterrence for both offender types. By creating impunity for elites, organised crime actors are attracted to the trade. Moreover, by amplifying sentencing inequalities, subsistence offenders are further impoverished, increasing re-offending. Corruption in wildlife justice thus drives significant long-term environmental, socioeconomic, and political harms, and erodes the rule of law.

Desafios e perspectivas na judicialização da saúde na América Latina: considerações sobre o fenômeno no Brasil e na Colômbia

Objective: The present study aims to understand how the judicialization of health develops as a global phenomenon, with particular emphasis on Latin American contexts, considering Brazil, and Colombia. Methodology: A literature review methodology supported by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses tool was adopted. The research was conducted using specific descriptors, and search strategies were carried out in the PubMed, MEDLINE, Lilacs, SciELO, CRD, CDSR, BVS, and BIREME databases. Articles published between 2010 and 2024 that discuss the judicialization of health, considering Portuguese, Spanish, or English languages, were selected. Results: The search resulted in 455 articles, of which 18 met the inclusion criteria. The analysis of the selected articles reveals the need for balanced solutions that respect individual rights without compromising the accessibility and quality of collective healthcare. The challenges imposed by judicialization are highlighted, such as issues of equity, financial sustainability of health systems, and resource prioritization. Conclusion: It concludes by emphasizing the importance of a multidisciplinary approach involving legislative adjustments, improvements in health system management, health education, and the promotion of equitable public policies. Submission: 05/29/24| Review: 08/16/24| Approval: 08/16/24

Development of an multicriteria decision analysis framework for rare disease reimbursement prioritization in Malaysia.

Rare diseases (RD)-related policies have received significant attention due to the pressing medical requirements associated with these medical conditions and the substantial impact and treatments they may have on healthcare budgets. Nevertheless, policymakers frequently encounter difficulties in managing issues concerning resource allocation and prioritization within this population. Realizing the need to address such problems, this study was conducted to develop a framework based on the multicriteria decision analysis to improve RD reimbursement prioritization in Malaysia. Primarily, a scoping review was performed to identify the methods and criteria used for the reimbursement of RD treatment, followed by strategic stakeholder engagement and a deliberative process on determining the best approach for the framework, including criteria identification, elicitation of weights, and a pilot assessment using the framework. The findings reflected the priorities and perspectives of the stakeholders, which identified eight key criteria and their associated weights, namely effectiveness (19.6 percent), disease severity (15.6 percent), safety (14.2 percent), access to treatment (12.6 percent), economic consideration (12.2 percent), type of therapeutic treatment (11.5 percent), availability of alternatives (8.3 percent), and population group (6 percent). In summary, the developed framework was well-accepted by the Rare Disease Committee, which will be applied as part of the committee deliberation for transparent and equitable decision making on fund allocation and reimbursement of orphan and RD treatment in Malaysia.

Resource prioritization and allocation in fog computing using hybrid optimization

With the rapid proliferation of IoT devices, the volume of data generated has reached unprecedented levels, necessitating efficient management strategies. Fog computing, complemented by 5G technologies, offers promising solutions to reduce service latency and enhance Quality of Service (QoS). However, allocating resources effectively remains challenging due to factors such as uncertainty, mobility, heterogeneity, and limited resources in fog computing environments. Traditional resource allocation (RA) algorithms often fall short of addressing these complexities. This study proposes a novel approach to RA in fog computing, utilizing a non-linear function to optimize resource allocation. An objective function is introduced, incorporating multi constraints such as resource utilization, service response rate, makespan, migration cost, and communication cost. The methodology emphasizes efficient resource allocation in crucial scenarios, facilitating rapid resource distribution where needed. The novel Coati Integrated Beluga Whale Optimization (CI-BWO) strategy is proposed to achieve optimal resource allocation in fog computing environments. By leveraging CI-BWO, this research aims to overcome the limitations of traditional RA methods and enhance the performance and scalability of fog computing applications. Finally, the superiority of the suggested strategy is assessed by comparison with many existing methods. When the task count is 200, the developed CI-BWO attained less migration cost of around 1.287, while existing models have acquired higher migration costs.

Chronospaces: An R package for the statistical exploration of divergence times promotes the assessment of methodological sensitivity

Abstract Much of our understanding of the history of life hinges upon time calibration, the process of assigning absolute times to cladogenetic events. Bayesian approaches to time‐scaling phylogenetic trees have dramatically grown in complexity, and depend today upon numerous methodological choices. Arriving at objective justifications for all of these is difficult and time‐consuming. Thus, divergence times are routinely inferred under only one or a handful of parametric conditions, often times chosen arbitrarily. Progress towards building robust biological timescales necessitates the development of better methods to visualize and quantify the sensitivity of results to these decisions. Here, we present an R package that assists in this endeavour through the use of chronospaces, that is, graphical representations summarizing variation in the node ages contained in time‐calibrated trees. We further test this approach by estimating divergence times for three empirical datasets—spanning widely differing evolutionary timeframes—using the software PhyloBayes. Our results reveal large differences in the impact of many common methodological decisions, with the choice of clock (uncorrelated vs autocorrelated) and loci having strong effects on inferred ages. Other decisions have comparatively minor consequences, including the use of the computationally intensive site‐heterogeneous model CAT‐GTR, whose effect might only be discernible for exceedingly old divergences (e.g. the deepest eukaryote nodes). The package chronospace implements a range of graphical and analytical tools that assist in the exploration of sensitivity and the prioritization of computational resources in the inference of divergence times.

Uncovering habitat associations and thresholds—insights for managing breeding waterfowl in Eastern Canada

ContextUnderstanding how habitat influences species abundance is crucial in developing ecologically sound wildlife conservation management plans. Exploring habitat associations and ecological thresholds in species’ responses allows for better conservation and management on a landscape-scale.ObjectivesThis work aimed to identify habitat drivers and response thresholds of waterfowl and waterbird species’ densities in eastern Canada to support key landscape-level decisions for habitat conservation and wetland management.MethodsWe developed predictive abundance models for 17 species across eastern Canada from 2001 to 2015 using data from four regional surveys and identified areas where prioritizing enhancement of wetlands would increase the breeding density of five priority waterfowl species.ResultsHabitat associations and spatial abundance patterns varied across species, but most species responded strongly to forest composition, agriculture, and wetland features. Threshold effects occurred and varied among species, yet generally once 14% of a plot was covered in wetlands, positive effects of increased wetland diminished for most species. Our results allow for the targeting of investments in increasing wetland area along portions of eastern Canada that provide the best opportunities to increase breeding densities for priority waterfowl species.ConclusionsUnderstanding species-habitat associations and response thresholds allows for landscape management and planning and prioritization of limited resources. We suggest that management and wetland enhancement efforts for waterfowl in eastern Canada should be guided by predictive models and response thresholds of key habitat attributes to best prioritize actions that will have the biggest positive impact on multiple species.

Spatial prioritization of groundwater resources in mountain region of Nepal: a GIS and AHP integrated framework

ABSTRACT Climate change and anthropogenic activities pose major challenges to water resource management in water-scarce mountain regions. Managing and monitoring groundwater effectively is crucial in mitigating these challenges. Understanding groundwater resources and their distribution is necessary for effective management. The present study covers Doramba rural municipality of Nepal. It employed the analytic hierarchy process (AHP) as a multi-criteria decision-making (MCDM) tool integrated with remote sensing (RS) data and geospatial techniques to identify groundwater potential zones (GPZs). The parameters for analysis include drainage density, slope, rainfall, lineament density, land use/land cover (LULC), soil, geology, curvature, roughness, topographic wetness index, and topographic position index. These 11 thematic layers of the study area were prioritized using Satty's AHP method for the delineation of GPZs. The results classified the study area into very high, high, moderate, low, and very low groundwater potential classes, covering 26.71, 27.98, 46.61, 34.71, and 4.89 km2, respectively. The delineated map accuracy was confirmed with a 70.7% prediction rate based on the spatial distribution of 56 springs in the study area. The study's results can be applied to the sustainable management of groundwater resources in the study region and other areas with similar hydrogeological conditions.

New Trauma Score versus Kampala Trauma Score II in predicting mortality following road traffic crash: a prospective multi-center cohort study

IntroductionMortality due to injuries disproportionately impact low income countries. Knowledge of who is at risk of poor outcomes is critical to guide resource allocation and prioritization of severely injured. Kampala Trauma Score (KTS), developed in 1996 and last modified in 2002 as KTS II, is still widely being used to predict injury outcomes in resource-limited settings with no further revisions in the past two decades, despite ongoing criticism of some of its parameters. The New Trauma Score (NTS), a recent development in 2017, has shown potential in mortality prediction, but a dearth of evidence exist regarding its performance in the African population.ObjectivesTo compare NTS to the modified Kampala Trauma Score (KTS II) in the prediction of 30-day mortality, and injury severity amongst patients sustaining road traffic crashes in Ugandan low-resource settings.MethodsMulti-center prospective cohort study of patients aged 15 years and above. Of the 194 participants, 85.1% were males with a mean age of 31.7 years. NTS and KTS II were determined for each participant within 30-minutes of admission and followed-up for 30 days to determine their injury outcomes. The sensitivity, specificity, and area under receiver operating characteristics curve (AUC) for predicting mortality were compared between the two trauma scores using SPSS version 22. Ethical clearance: Research and Ethics Committee of Kampala International University Western Campus (Ref No: KIU-2022-125).ResultsThe injury severity classifications based on NTS vs. KTS II were mild (55.7% vs. 25.8%), moderate (29.9% vs. 30.4%), and severe (14.4% vs. 43.8%). The mortality rates for each injury severity category based on NTS vs. KTS II were mild (0.9% v 0%), moderate (20.7% vs. 5.1%), and severe (50% vs. 28.2%). The AUC was 0.87 for NTS (95% CI 0.808–0.931) vs. 0.86 (95% CI 0.794–0.919) for KTS II respectively. The sensitivity of NTS vs. KTS II in predicting mortality was 92.6% (95% CI: 88.9–96.3) vs. 70.4% (95% CI: 63.0-77.8) while the specificity was 70.7% (95% CI: 64.2–77.2) vs. 78.4% (95% CI: 72.1–84.7) at cut off points of 17 for NTS and 6 for KTS II respectively.ConclusionsNTS was more sensitive but its specificity for purposes of 30-day mortality prediction was lower compared to KTS II. Thus, in low-resourced trauma environment where time constraints and pulse oximeters are of concern, KTS II remains superior to NTS.

How to Obtain a Sustainably Preferential Supplier Resource Allocation? A Model Based on the S-O-R Framework from a Supplier’s Perspective

As pivotal members of upstream channels, suppliers wield significant influence on supply chains’ competitive advantage through their abundant resources. Buyers often engage in supplier development to access scarce resources, yet the motivations guiding suppliers’ responses remain unclear. This study elucidates suppliers’ cognitive processes and the factors influencing their decisions to allocate resources. Utilizing the S-O-R framework, we construct a moderated mediation model to verify motivational drivers and explore interactions among mediators. A survey involving 246 Chinese manufacturing suppliers was conducted, employing path analysis with bootstrap validation. It aimed to confirm how supplier-perceived relationship value, trust, and switching costs mediate between supplier development and resource allocation decisions. Our study delves into how supplier development impacts resource allocation, emphasizing psychological motivation within the S-O-R framework. We analyze three dimensions of supplier value perception: relationship value, trust, and switching costs. These factors influence physical and innovation resource allocation differently. While all prompt the prioritization of physical resources, only relationship value encourages innovative resource sharing, with switching costs hindering this. Supplier-perceived trust acts as both a mediator and moderator, enhancing positive effects on physical allocation and mitigating negative impacts on innovation resource sharing. This study advances the S-O-R framework’s applicability, providing novel insights into supplier resource allocation.

Biophysical Resource Characterization, Identification and Prioritization of Major Constraints and Potentials of Gara Ebanu Community Watershed in Sululta District, Ethiopia

Baseline characterization helps to understand the initial livelihood condition of the people in the watershed before and after the intervention of the project to measure the amount of change attained by the project. The objective of the study was to characterize baseline information on the existing biophysical resource used as benchmark for planning and impact monitoring and to identify and major constraints and potential in the watershed. The watershed was selected depending on agro-ecological representation, prevalence of resource management and land degradation problems and accessibility for intensive follow-up. Based on the preliminary outlet identified during the watershed selection process, the watershed boundary was delineated using GPS data and the map of watershed was geo-referenced and digitized for its contour, roads, rivers, and other features. Both primary and secondary data were used for the study and primary data was collected through field observation, household survey, focus group discussion and interview of the key informants. From the total 103 household heads living in the watershed, 62 household heads were selected as a respondent for the study. The collected data was managed and analyzed using Statistical Package for Social Sciences (SPSS) and Microsoft excel 2010. The results of the study showed that about 29.4% of the watershed slope was characterized by flat lands, 45.7% moderate slope and 24.9% steep lands. As well as soil fertility status of cultivated land in the watershed were 33.9% low, 55.9% moderate and 10.2% high. The results of the study showed that the major constraints identified by sampled household heads were decline of soil fertility, soil erosion, climate change, land shortage, and deforestation were significantly contributed to the low crop yield in the watershed. About 27.4% of the sampled households had encountered decline of soil fertility problems, 25.8% of sample farmers encountered soil erosion problem, and 22.6% of sample farmers encountered climate change problem in the watershed. From the identified major constraints, the highest priorities were given for decline of soil fertility, soil erosion problem, shortage of feed and fodder, and decline of crop productivity respectively. In the watershed, immediate short-term actions should be taken particularly participatory integrated watershed management were recommended.

The Influence of Industry 4.0 on Work Behavior and Employee Productivity in Technology Companies

Industry 4.0, characterized by automation, artificial intelligence (AI), and big data, has revolutionized work dynamics and productivity in technology firms. This transformation necessitates employees to acquire new skills and adapt to a more agile and intricate work setting. This study employs the Systematic Literature Review (SLR) method to scrutinize how Industry 4.0 influences work behavior and employee productivity. The SLR method gathers and analyzes pertinent literature from diverse sources such as scientific journals, books, and industry reports, applying inclusion and exclusion criteria to ensure relevance and reliability. A qualitative approach identifies key themes from the literature. The analysis reveals that Industry 4.0 induces substantial shifts in work behavior, emphasizing the importance of flexibility and adeptness with new technologies. Enhancing digital skills through training is crucial to boosting productivity. Transformational leadership emerges as a catalyst for fostering employee motivation and performance. Effective human resource management and prioritization of employee well-being are also pivotal in navigating challenges and harnessing opportunities associated with Industry 4.0. The adoption of Industry 4.0 technology yields significant benefits in enhancing employee productivity and operational efficiency. However, it necessitates profound changes in HR management strategies and skills development. Technology firms must invest in cultivating digital competencies, implementing transformational leadership practices, and cultivating supportive work environments to fully capitalize on this transformation. These insights furnish actionable guidance for companies seeking to optimize employee productivity in the digital age.

The Fate of the Abstract: Presentation and Publication Characteristics of Abstracts Presented at the Society of Gynecologic Surgeons Annual Scientific Meetings 2013 to 2020

Study ObjectiveTo evaluate what proportion of abstracts presented at the Society of Gynecologic Surgeons (SGS) Annual Scientific Meetings went on to be published in publicly available journals. DesignRetrospective observational study. SettingSingle organization. ParticipantsAbstracts (oral presentations, oral posters, video presentations, nonoral posters) presented at the SGS Annual Scientific Meeting from 2013 to 2020. InterventionsVariables were collected pertaining to abstract authors, study type, timing of the session presented, and journal factors. To identify possible publications, abstracts were cross-referenced in PubMed and Google Scholar. Measurements and Main ResultsA total of 912 abstracts were reviewed: 155 oral presentations, 184 oral posters, 79 video presentations, and 490 nonoral posters. 45.8% of abstracts went on to publication in a peer-reviewed journal. Most abstracts (75.0%) were published from institutions with a fellowship presence and at a university-based program (71.5%). The five most represented institutions presented 27.5% of all abstracts during an SGS session. Oral presentations were more likely than oral posters to be structured as randomized controlled trials (20% vs 9%, p = .028), and to be published in a journal with a higher impact factor (6.36 ± 11.74 vs 3.88 ± 2.72, p = .031). Type of presentation and fellowship presence significantly affected the likelihood of abstract publication (oral presentation OR 0.73, 95% CI [0.466, 1.141], p = .167; video OR 0.14, 95% CI [0.075, 0.261; nonoral poster OR 0.30, 95% CI [0.204, 0.439]; p < .001; fellowship OR 1.62, 95% CI [1.167, 2.237], p = .004). ConclusionOver 8 years of the SGS Annual Scientific Meeting, the rate of abstract publication was 45.8%. Abstract origination from an academic institution with a fellowship program significantly affected the likelihood of publication. Abstract presentation at a society meeting is a prestigious opportunity, and prioritization of resources and elimination of barriers should be encouraged to further promote progression of these projects to publication.

The impact of hospital saturation on non-COVID-19 hospital mortality during the pandemic in France: a national population-based cohort study

BackgroundA previous study reported significant excess mortality among non-COVID-19 patients due to disrupted surgical care caused by resource prioritization for COVID-19 cases in France. The primary objective was to investigate if a similar impact occurred for medical conditions and determine the effect of hospital saturation on non-COVID-19 hospital mortality during the first year of the pandemic in France.MethodsWe conducted a nationwide population-based cohort study including all adult patients hospitalized for non-COVID-19 acute medical conditions in France between March 1, 2020 and 31 May, 2020 (1st wave) and September 1, 2020 and December 31, 2020 (2nd wave). Hospital saturation was categorized into four levels based on weekly bed occupancy for COVID-19: no saturation (< 5%), low saturation (> 5% and ≤ 15%), moderate saturation (> 15% and ≤ 30%), and high saturation (> 30%). Multivariate generalized linear model analyzed the association between hospital saturation and mortality with adjustment for age, sex, COVID-19 wave, Charlson Comorbidity Index, case-mix, source of hospital admission, ICU admission, category of hospital and region of residence.ResultsA total of 2,264,871 adult patients were hospitalized for acute medical conditions. In the multivariate analysis, the hospital mortality was significantly higher in low saturated hospitals (adjusted Odds Ratio/aOR = 1.05, 95% CI [1.34–1.07], P < .001), moderate saturated hospitals (aOR = 1.12, 95% CI [1.09–1.14], P < .001), and highly saturated hospitals (aOR = 1.25, 95% CI [1.21–1.30], P < .001) compared to non-saturated hospitals. The proportion of deaths outside ICU was higher in highly saturated hospitals (87%) compared to non-, low- or moderate saturated hospitals (81–84%). The negative impact of hospital saturation on mortality was more pronounced in patients older than 65 years, those with fewer comorbidities (Charlson 1–2 and 3 vs. 0), patients with cancer, nervous and mental diseases, those admitted from home or through the emergency room (compared to transfers from other hospital wards), and those not admitted to the intensive care unit.ConclusionsOur study reveals a noteworthy “dose-effect” relationship: as hospital saturation intensifies, the non-COVID-19 hospital mortality risk also increases. These results raise concerns regarding hospitals’ resilience and patient safety, underscoring the importance of identifying targeted strategies to enhance resilience for the future, particularly for high-risk patients.

Understanding Barriers to Guideline-Concordant Treatment in Foregut Cancer: From Data to Solutions

BackgroundA large proportion of patients with foregut cancers do not receive guideline-concordant treatment (GCT). This study sought to understand underlying barriers to GCT through a root cause analysis approach.MethodsA single-institution retrospective review of 498 patients with foregut (gastric, pancreatic, and hepatobiliary) adenocarcinoma from 2018 to 2022 was performed. Guideline-concordant treatment was defined based on National Comprehensive Cancer Network guidelines. The Ishikawa cause and effect model was used to establish main contributing factors to non-GCT.ResultsOverall, 34% did not receive GCT. Root causes of non-GCT included Patient, Physician, Institutional Environment and Broader System-related factors. In decreasing order of frequency, the following contributed to non-GCT: receipt of incomplete therapy (N = 28, 16.5%), deconditioning on chemotherapy (N = 26, 15.3%), delays in care because of patient resource constraints followed by loss to follow-up (N = 19, 11.2%), physician factors (N = 19, 11.2%), no documentation of treatment plan after referral to oncologic expertise (N = 19, 11.2%), loss to follow-up before oncology referral (N = 17, 10%), nonreferral to medical oncologic expertise (N = 16, 9.4%), nonreferral to surgical oncology in patients with resectable disease (N = 15, 8.8%), and complications preventing completion of treatment (N = 11, 6.5%). Non-GCT often was a function of multiple intersecting patient, physician, and institutional factors.ConclusionsA substantial percentage of patients with foregut cancer do not receive GCT. Solutions that may improve receipt of GCT include development of automated systems to improve patient follow-up; institutional prioritization of resources to enhance staffing; financial counseling and assistance programs; and development and integration of structured prehabilitation programs into cancer treatment pathways.

A prognostic model for use before elective surgery to estimate the risk of postoperative pulmonary complications (GSU-Pulmonary Score): a development and validation study in three international cohorts

Pulmonary complications are the most common cause of death after surgery. This study aimed to derive and externally validate a novel prognostic model that can be used before elective surgery to estimate the risk of postoperative pulmonary complications and to support resource allocation and prioritisation during pandemic recovery. Data from an international, prospective cohort study were used to develop a novel prognostic risk model for pulmonary complications after elective surgery in adult patients (aged ≥18 years) across all operation and disease types. The primary outcome measure was postoperative pulmonary complications at 30 days after surgery, which was a composite of pneumonia, acute respiratory distress syndrome, and unexpected mechanical ventilation. Model development with candidate predictor variables was done in the GlobalSurg-CovidSurg Week dataset (global; October, 2020). Two structured machine learning techniques were explored (XGBoost and the least absolute shrinkage and selection operator [LASSO]), and the model with the best performance (GSU-Pulmonary Score) underwent internal validation using bootstrap resampling. The discrimination and calibration of the score were externally validated in two further prospective cohorts: CovidSurg-Cancer (worldwide; February to August, 2020, during the COVID-19 pandemic) and RECON (UK and Australasia; January to October, 2019, before the COVID-19 pandemic). The model was deployed as an online web application. The GlobalSurg-CovidSurg Week and CovidSurg-Cancer studies were registered with ClinicalTrials.gov, NCT04509986 and NCT04384926. Prognostic models were developed from 13 candidate predictor variables in data from 86 231 patients (1158 hospitals in 114 countries). External validation included 30 492 patients from CovidSurg-Cancer (726 hospitals in 75 countries) and 6789 from RECON (150 hospitals in three countries). The overall rates of pulmonary complications were 2·0% in derivation data, and 3·9% (CovidSurg-Cancer) and 4·7% (RECON) in the validation datasets. Penalised regression using LASSO had similar discrimination to XGBoost (area under the receiver operating curve [AUROC] 0·786, 95% CI 0·774-0·798 vs 0·785, 0·772-0·797), was more explainable, and required fewer covariables. The final GSU-Pulmonary Score included ten predictor variables and showed good discrimination and calibration upon internal validation (AUROC 0·773, 95% CI 0·751-0·795; Brier score 0·020, calibration in the large [CITL] 0·034, slope 0·954). The model performance was acceptable on external validation in CovidSurg-Cancer (AUROC 0·746, 95% CI 0·733-0·760; Brier score 0·036, CITL 0·109, slope 1·056), but with some miscalibration in RECON data (AUROC 0·716, 95% CI 0·689-0·744; Brier score 0·045, CITL 1·040, slope 1·009). This novel prognostic risk score uses simple predictor variables available at the time of a decision for elective surgery that can accurately stratify patients' risk of postoperative pulmonary complications, including during SARS-CoV-2 outbreaks. It could inform surgical consent, resource allocation, and hospital-level prioritisation as elective surgery is upscaled to address global backlogs. National Institute for Health Research.