The impact of intellectual capital on start-up expectations

2016 ◽  
Vol 17 (4) ◽  
pp. 654-674 ◽  
Author(s):  
Diego Matricano
Keyword(s):  
Logistic Regression ◽  
Intellectual Capital ◽  
Regression Models ◽  
Empirical Studies ◽  
Binary Logistic Regression ◽  
Research Trend ◽  
Content Type ◽  
Logistic Regression Models ◽  
Start Up ◽  
The Impact

Purpose According to an emerging research trend, which seeks to apply the concept of intellectual capital (IC) to the field of entrepreneurship, the purpose of this paper is to test whether IC can affect the start-up expectations of aspiring entrepreneurs. Design/methodology/approach Binary logistic regression models, based on empirical data derived from the Global Entrepreneurship Monitor website and referring to Italy over the years 2005-2010, are used to test the influence of IC (comprising human, structural and relational capital) on start-up expectations. Findings Binary logistic regression models reveal robust results. Human, structural and relational capitals affect start-up expectations in Italy. Only in 2010 did structural capital fail to do so. Research limitations/implications This study has three main limitations. The first concerns the need for further research to confirm the influence of IC on start-up expectations. The second concerns in-depth, more exhaustive analyses that cannot be carried out due to the use of second- hand data. The third deals with the reference only to Italy, over a limited time-span (2005-2010). Originality/value To the best knowledge of the author, this is one of the first empirical studies that investigate whether IC can affect start-up expectations. Results revealed by the regression models might steer other scholars’ interest toward this research path (linking IC and entrepreneurship) that has not yet been properly considered.

scholarly journals The impact of demographic, clinical, genetic, and imaging variables on tau PET status

2020 ◽  
Author(s):  
Rik Ossenkoppele ◽  
◽  
Antoine Leuzy ◽  
Hanna Cho ◽  
Carole H. Sudre ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cortical Thickness ◽  
Regression Models ◽  
Neurodegenerative Disorder ◽  
Amyloid Β ◽  
Binary Logistic Regression ◽  
Clinical Genetic ◽  
Logistic Regression Models ◽  
Tau Pet ◽  
The Impact

Abstract Purpose A substantial proportion of amyloid-β (Aβ)+ patients with clinically diagnosed Alzheimer’s disease (AD) dementia and mild cognitive impairment (MCI) are tau PET–negative, while some clinically diagnosed non-AD neurodegenerative disorder (non-AD) patients or cognitively unimpaired (CU) subjects are tau PET–positive. We investigated which demographic, clinical, genetic, and imaging variables contributed to tau PET status. Methods We included 2338 participants (430 Aβ+ AD dementia, 381 Aβ+ MCI, 370 non-AD, and 1157 CU) who underwent [18F]flortaucipir (n = 1944) or [18F]RO948 (n = 719) PET. Tau PET positivity was determined in the entorhinal cortex, temporal meta-ROI, and Braak V-VI regions using previously established cutoffs. We performed bivariate binary logistic regression models with tau PET status (positive/negative) as dependent variable and age, sex, APOEε4, Aβ status (only in CU and non-AD analyses), MMSE, global white matter hyperintensities (WMH), and AD-signature cortical thickness as predictors. Additionally, we performed multivariable binary logistic regression models to account for all other predictors in the same model. Results Tau PET positivity in the temporal meta-ROI was 88.6% for AD dementia, 46.5% for MCI, 9.5% for non-AD, and 6.1% for CU. Among Aβ+ participants with AD dementia and MCI, lower age, MMSE score, and AD-signature cortical thickness showed the strongest associations with tau PET positivity. In non-AD and CU participants, presence of Aβ was the strongest predictor of a positive tau PET scan. Conclusion We identified several demographic, clinical, and neurobiological factors that are important to explain the variance in tau PET retention observed across the AD pathological continuum, non-AD neurodegenerative disorders, and cognitively unimpaired persons.

The impact of education to the transition from unemployment to employment in Egypt

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amany Yashoa Gad
Keyword(s):  
Logistic Regression ◽  
Labor Market ◽  
Labor Force ◽  
Regression Models ◽  
Transition Probabilities ◽  
Educational Levels ◽  
Content Type ◽  
Logistic Regression Models ◽  
The Impact ◽  
Different Levels

PurposeThis paper aims to identify the level of contribution of different levels of education to remaining in unemployment as well as the transition from unemployment to employment in Egypt.Design/methodology/approachIn this paper, transition probabilities matrix differentiated by gender, age groups, educational levels, marital status and place of residence based on worker flows across employment, unemployment and out of labor force states during the period 2012–2018 using Egypt Labor Market Panel Survey of 2018. The results point to the highly static nature of the Egyptian labor market. Employment and the out of labor force states are the least mobile among labor market states. This is because employment state is very desirable and the out of labor force is the largest labor market states, especially for females. Also, this study examines the impact of different educational levels separately on remaining in unemployment and transition from unemployment to employment state using eight binary logistic regression models.FindingsThe main results of transitions from unemployment to employment are relatively large for males, elder-age, uneducated workers as well as workers who are not married and urban residents, and the results of the logistic regression models consistent with the transition probabilities matrix results, except for few cases. Based on the above findings, there is enough evidence to accept the null hypothesis that no education has a positive significant impact to transition unemployed individuals from unemployment to employment, while less than intermediate as well as higher education have a negative significant impact to transition unemployed individuals from unemployment to employment.Originality/valueThis paper proposes to address the problem of the unemployment among highly educated which is much higher compared with illiterates and try to understand the impact of different levels of education separately on the transition from unemployment to employment, to help the policymakers to eradicate the gap between education and the demand of the labor market in Egypt.

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

scholarly journals EPID-34. THE DETRIMENTAL EFFECT OF BIOPSY PRECEDING RESECTION IN SURGICALLY ACCESSIBLE GLIOBLASTOMAS: RESULTS FROM THE NATIONAL CANCER DATABASE

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii85-ii86
Author(s):  
Ping Zhu ◽  
Xianglin Du ◽  
Angel Blanco ◽  
Leomar Y Ballester ◽  
Nitin Tandon ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Failure Time ◽  
Binary Logistic Regression ◽  
Rank Test ◽  
Imaging Features ◽  
National Cancer Database ◽  
Prolonged Length ◽  
Logistic Regression Models ◽  
Operative Outcomes ◽  
The Impact

Abstract OBJECTIVES To investigate the impact of biopsy preceding resection compared to upfront resection in glioblastoma overall survival (OS) and post-operative outcomes using the National Cancer Database (NCDB). METHODS A total of 17,334 GBM patients diagnosed between 2010 and 2014 were derived from the NCDB. Patients were categorized into two groups: “upfront resection” versus “biopsy followed by resection”. Primary outcome was OS. Post-operative outcomes including 30-day readmission/mortality, 90-day mortality, and prolonged length of inpatient hospital stay (LOS) were secondary endpoints. Kaplan-Meier methods and accelerated failure time (AFT) models with gamma distribution were applied for survival analysis. Multivariable binary logistic regression models were performed to compare differences in the post-operative outcomes between these groups. RESULTS Patients undergoing “upfront resection” experienced superior survival compared to those undergoing “biopsy followed by resection” (median OS: 12.4 versus 11.1 months, log-rank test: P=0.001). In multivariable AFT models, significant survival benefits were observed among patients undergoing “upfront resection” (time ratio [TR]: 0.83, 95% CI: 0.75–0.93, P=0.001). Patients undergoing upfront GTR had the longest survival compared to upfront STR, GTR following STR, or GTR and STR following an initial biopsy (14.4 vs. 10.3, 13.5, 13.3, and 9.1, months), respectively (TR: 1.00 [Ref.], 0.75, 0.82, 0.88, and 0.67). Recent years of diagnosis, higher income and treatment at academic facilities were significantly associated with the likelihood of undergoing upfront resection after adjusting the covariates. Multivariable logistic regression revealed that 30-day mortality and 90-day mortality were decreased by 73% and 44% for patients undergoing “upfront resection” over “biopsy followed by resection”, respectively (both p &lt; 0.001). CONCLUSIONS Pre-operative biopsies for surgically accessible tumors with characteristic imaging features of Glioblastoma lead to worse survival despite subsequent resection compared to patients undergoing upfront resection.

scholarly journals High association of COVID-19 severity with poor gut health score in Lebanese patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258913
Author(s):  
Imad Al Kassaa ◽  
Sarah El Omari ◽  
Nada Abbas ◽  
Nicolas Papon ◽  
Djamel Drider ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Equal Proportion ◽  
Gut Health ◽  
Kendall’S Tau ◽  
High Association ◽  
Kendall's Tau ◽  
Health Score ◽  
Logistic Regression Models ◽  
The Impact

Background Coronavirus disease 2019 (COVID-19) has affected millions of lives globally. However, the disease has presented more extreme challenges for developing countries that are experiencing economic crises. Studies on COVID-19 symptoms and gut health are scarce and have not fully analyzed possible associations between gut health and disease pathophysiology. Therefore, this study aimed to demonstrate a potential association between gut health and COVID-19 severity in the Lebanese community, which has been experiencing a severe economic crisis. Methods This cross-sectional study investigated SARS-CoV-2 PCR-positive Lebanese patients. Participants were interviewed and gut health, COVID-19 symptoms, and different metrics were analyzed using simple and multiple logistic regression models. Results Analysis of the data showed that 25% of participants were asymptomatic, while an equal proportion experienced severe symptoms, including dyspnea (22.7%), oxygen need (7.5%), and hospitalization (3.1%). The mean age of the participants was 38.3 ±0.8 years, and the majority were males (63.9%), married (68.2%), and currently employed (66.7%). A negative correlation was found between gut health score and COVID-19 symptoms (Kendall’s tau-b = -0.153, P = 0.004); indicating that low gut health was associated with more severe COVID-19 cases. Additionally, participants who reported unhealthy food intake were more likely to experience severe symptoms (Kendall’s tau-b = 0.118, P = 0.049). When all items were taken into consideration, multiple ordinal logistic regression models showed a significant association between COVID-19 symptoms and each of the following variables: working status, flu-like illness episodes, and gut health score. COVID-19 severe symptoms were more common among patients having poor gut health scores (OR:1.31, 95%CI:1.07–1.61; P = 0.008), experiencing more than one episode of flu-like illness per year (OR:2.85, 95%CI:1.58–5.15; P = 0.001), and owning a job (OR:2.00, 95%CI:1.1–3.65; P = 0.023). Conclusions To our knowledge, this is the first study that showed the impact of gut health and exposure to respiratory viruses on COVID-19 severity in Lebanon. These findings can facilitate combating the pandemic in Lebanon.

Abstract P122: The Impact of Center Certification Status on Acute Stroke Care Within a Multi-Hospital System in Western United States

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Radoslav I Raychev ◽  
CrystalAnn Moreno ◽  
Leslie Corless ◽  
Jason W Tarpley ◽  
John F Zurasky ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Clinical Outcome ◽  
Regression Models ◽  
Process Of Care ◽  
Chi Square ◽  
Hospital System ◽  
Logistic Regression Models ◽  
Stroke Center ◽  
The Impact ◽  
Iv Tpa

Introduction: We aimed to investigate the impact of certification status on process of care metrics and clinical outcome in a large multi-center hospital system. Methods: We analyzed data obtained from the Providence Stroke Registry between January 2016 and December 2019. Key process of care metrics and clinical outcome were compared among patients with a discharge diagnosis of stroke and stratified based on site certification: comprehensive stroke center (CSC), thrombectomy-capable stroke center (TSC), primary stroke center (PSC) and no certification (NC). Donner’s adjusted chi-square tests were used to compare proportions for each metric grouped by certification. Generalized linear mixed effects logistic regression models were used to adjust for mode of patient arrival, age, sex, admit NIHSS, and medical history. Results: Data included 45,278 patients. Results from the analyses are summarized in the table. Donner’s adjusted chi-square analyses showed significant differences for metrics across certification groups. Results from the logistic regression models indicated significant differences in IV TPA and EVT treatment, as well as IV TPA treatment times across certification groups. There were no significant differences between TSC and CSC. Conclusions: Patients presenting with acute ischemic stroke at NC and PSC were significantly less likely to receive IV TPA or EVT with significantly less efficient IV tPA treatment times as compared to CSC. However, CSC and TSC sites performed similarly.

scholarly journals Application of Logistic Regression Models for the Marketability of Cucumber Cultivars

Agronomy ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 17 ◽  
Author(s):  
Manuel Díaz-Pérez ◽  
Ángel Carreño-Ortega ◽  
José-Antonio Salinas-Andújar ◽  
Ángel-Jesús Callejón-Ferre
Keyword(s):  
Weight Loss ◽  
Logistic Regression ◽  
Shelf Life ◽  
Regression Models ◽  
Chilling Injury ◽  
Binary Logistic Regression ◽  
Fruit Weight ◽  
Binary Logistic Regression Analysis ◽  
Cucumis Sativus L ◽  
Logistic Regression Models

The aim of this study is to establish a binary logistic regression method to evaluate and select cucumber cultivars (Cucumis sativus L.) with a longer postharvest shelf life. Each sample was evaluated for commercial quality (fruit aging, weight loss, wilting, yellowing, chilling injury, and rotting) every 7 days of storage. Simple and multiple binary logistic regression models were applied in which the dependent variable was the probability of marketability and the independent variables were the days of storage, cultivars, fruit weight loss, and months of evaluation. The results showed that cucumber cultivars with a longer shelf life can be selected by a simple and multiple binary logistic regression analysis. Storage time was the main determinant of fruit marketability. Fruit weight loss strongly influenced the probability of marketability. The logistic model allowed us to determine the cucumber weight loss percentage over which a fruit would be rejected in the market.

Innovation in the aftermath of downsizing: evidence from the threat-rigidity perspective

2020 ◽  
Vol 49 (9) ◽  
pp. 1859-1877
Author(s):  
José Fernández-Menéndez ◽  
Óscar Rodríguez-Ruiz ◽  
José-Ignacio López-Sánchez ◽  
María Isabel Delgado-Piña
Keyword(s):  
Regression Models ◽  
Negative Impact ◽  
Content Type ◽  
Logistic Regression Models ◽  
Marketing Innovation ◽  
Different Types ◽  
Threat Rigidity ◽  
The Impact ◽  
Research Show ◽  
Innovation Outcomes

PurposeThe purpose of this paper is to study how job reductions affect product innovation and marketing innovation in a sample of 2,034 Spanish manufacturing firms in the period 2007–2014.Design/methodology/approachPoisson and logistic regression models with random effects were used to analyse the impact of downsizing on some innovation outcomes of firms.FindingsThe results of this research show that the stressful measure of job reductions may have unexpected consequences, stimulating innovation. However downsizing combined with radical organisational changes such as new equipment, techniques or processes seems to have a negative impact on product and marketing innovation.Originality/valueThis research has two original features. First, it explores the unconventional direction of causality from the planned elimination of jobs to innovation outputs. Secondly, the paper looks at the combined effect of downsizing and other restructuring measures on different types of innovation. Following the threat-rigidity theory, we assume that this combination represents a major threat for survivors that leads to lower levels of product and marketing innovation.

scholarly journals The sensitivity of modeled snow accumulation and melt to precipitation phase methods across a climatic gradient

2019 ◽  
Vol 23 (9) ◽  
pp. 3765-3786 ◽  
Author(s):  
Keith S. Jennings ◽  
Noah P. Molotch
Keyword(s):  
Logistic Regression ◽  
Air Temperature ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Snow Accumulation ◽  
Phase Method ◽  
Logistic Regression Models ◽  
Temperature Thresholds ◽  
Snow Cover Duration ◽  
Precipitation Phase

Abstract. A critical component of hydrologic modeling in cold and temperate regions is partitioning precipitation into snow and rain, yet little is known about how uncertainty in precipitation phase propagates into variability in simulated snow accumulation and melt. Given the wide variety of methods for distinguishing between snow and rain, it is imperative to evaluate the sensitivity of snowpack model output to precipitation phase determination methods, especially considering the potential of snow-to-rain shifts associated with climate warming to fundamentally change the hydrology of snow-dominated areas. To address these needs we quantified the sensitivity of simulated snow accumulation and melt to rain–snow partitioning methods at sites in the western United States using the SNOWPACK model without the canopy module activated. The methods in this study included different permutations of air, wet bulb and dew point temperature thresholds, air temperature ranges, and binary logistic regression models. Compared to observations of snow depth and snow water equivalent (SWE), the binary logistic regression models produced the lowest mean biases, while high and low air temperature thresholds tended to overpredict and underpredict snow accumulation, respectively. Relative differences between the minimum and maximum annual snowfall fractions predicted by the different methods sometimes exceeded 100 % at elevations less than 2000 m in the Oregon Cascades and California's Sierra Nevada. This led to ranges in annual peak SWE typically greater than 200 mm, exceeding 400 mm in certain years. At the warmer sites, ranges in snowmelt timing predicted by the different methods were generally larger than 2 weeks, while ranges in snow cover duration approached 1 month and greater. Conversely, the three coldest sites in this work were relatively insensitive to the choice of a precipitation phase method, with average ranges in annual snowfall fraction, peak SWE, snowmelt timing, and snow cover duration of less than 18 %, 62 mm, 10 d, and 15 d, respectively. Average ranges in snowmelt rate were typically less than 4 mm d−1 and exhibited a small relationship to seasonal climate. Overall, sites with a greater proportion of precipitation falling at air temperatures between 0 and 4 ∘C exhibited the greatest sensitivity to method selection, suggesting that the identification and use of an optimal precipitation phase method is most important at the warmer fringes of the seasonal snow zone.

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