Alternative Predictors Research Articles

Appointment Nonadherence and Graft Outcomes in Living Donor Kidney Transplantation

Medication nonadherence is associated with worse graft outcomes but is hard to recognize in clinical settings due to its self-reporting nature. We hypothesized that appointment nonadherence might be associated with worse graft outcomes in living donor kidney transplantation. We included 167 adult living-donor kidney transplants whose grafts survived >2 years from April 2011 to May 2020. Thirty-two cases of appointment nonadherence were identified and compared with the controls (n=135). Younger age, male sex, higher body weight, and parent donor were significantly observed in the appointment nonadherence group. The appointment nonadherence group was significantly associated with worse graft survival (5 years: 82.3% vs 98.9%, P < .001, 10 years: 67.2% vs 89.6%, P < .001), de novo donor-specific antibody production, acute rejection, as well as the decline of graft function. Furthermore, appointment nonadherence had a 4-fold higher risk of graft loss after an adjustment with recipient age, sex, body weight, and donor type (adjusted hazard ratio: 3.93, 95% CI: 1.15-13.42, P=.029). Appointment nonadherence might be an alternative predictor for worse graft outcomes after living donor kidney transplantation.

Radiation Oncology Resident Evaluations: Current Methods and Resident Perceptions

This study analyzes assessments within radiation oncology (RO) to determine characteristics of existing assessment methods and then report resident perceptions of these methods. We hypothesize familiarity with evaluation methods is predictive of the perceived utility of evaluations and behavioral changes. This study was conducted in 2 phases. Phase 1 involved requesting resident evaluation forms from RO residency programs to assess the Accreditation Council for Graduate Medical Education 6 Core Competencies. Analysis of variance was used to determine any significant differences between institutions or categories of questions. In phase 2, RO residents were surveyed about familiarity with the Accreditation Council for Graduate Medical Education Milestones and their perceptions about the current methods. Responses to questions were further analyzed using linear regression models. In phase 1, forms were obtained from 13 institutions, and they were based on the 6 Core Competencies with an average of 19 questions (standard deviation, 11; range, 5-47) in total. Analysis of variance did not show significant variation in the number of questions between the categories (F=0.78, P=.6). A significant difference in the mean number of questions used for assessing each of the competencies was found between institutions (F=6.6, P < .01). In phase 2, a majority of surveyed residents reported being "not" or only "slightly familiar" with the competencies (59.6%) and the factors used to assess them (73.1%). Resident-reported familiarity with the evaluation methods was not found to be a significant predictor of likelihood of changing postevaluation (coefficient=0.41, P=.204), intimidation from receiving evaluations (coefficient=-0.06, P=.792), stress of receiving evaluations (coefficient=-0.11, P=.62), or usefulness of evaluations (coefficient=-0.02, P=.83). Familiarity with evaluation methods is not correlated with perceptions or behavioral changes necessitating further investigation of alternative predictor variables. Despite the low familiarity with evaluation tools, most residents reported that evaluations were useful and likely to elicit changes in their behaviors and practice, highlighting the value of current evaluation methods.

A literature survey of corporate failure prediction models

PurposeThis study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.Design/methodology/approachThis study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.FindingsThere are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.Originality/valueThe study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

A direct comparison of ecological theories for predicting the relationship between plant traits and growth.

Despite long-standing theory for classifying plant ecological strategies, limited data directly link organismal traits to whole-plant growth rates (GRs). We compared trait-growth relationships based on three prominent theories: growth analysis, Grime's competitive-stress tolerant-ruderal (CSR) triangle, and the leaf economics spectrum (LES). Under these schemes, growth is hypothesized to be predicted by traits related to relative biomass investment, leaf structure, or gas exchange, respectively. We also considered traits not included in these theories but that might provide potential alternative best predictors of growth. In phylogenetic analyses of 30 diverse milkweeds (Asclepias spp.) and 21 morphological and physiological traits, GR (total biomass produced per day) varied 50-fold and was best predicted by biomass allocation to leaves (as predicted by growth analysis) and the CSR traits of leaf size and leaf dry matter content. Total leaf area (LA) and plant height were also excellent predictors of whole-plant GRs. Despite two LES traits correlating with growth (mass-based leaf nitrogen and area-based leaf phosphorus contents), these were in the opposite direction of that predicted by LES, such that higher N and P contents corresponded to slower growth. The remaining LES traits (e.g., leaf gas exchange) were not predictive of plant GRs. Overall, differences in GR were driven more by whole-plant characteristics such as biomass fractions and total LA than individual leaf-level traits such as photosynthetic rate or specific leaf area. Our results are most consistent with classical growth analysis-combining leaf traits with whole-plant allocation to best predict growth. However, given that destructive biomass measures are often not feasible, applying easy-to-measure leaf traits associated with the CSR classification appear more predictive of whole-plant growth than LES traits. Testing the generality of this result across additional taxa would further improve our ability to predict whole-plant growth from functional traits across scales.

EU Climate Change News Index: Forecasting EU ETS prices with online news

Carbon prices have been rapidly increasing in the EU since 2018 and accurate forecasting of EU Emissions Trading System (ETS) prices has become essential. This paper proposes a novel method to generate alternative predictors for daily ETS price returns using relevant online news information. We devise the EU Climate Change News Index by calculating the term frequency–inverse document frequency (TF–IDF) feature for climate change-related keywords. The index is capable of tracking the ongoing debate about climate change in the EU. Finally, we show that incorporating the index in a simple predictive model significantly improves forecasts of ETS price returns.

Optimization of Transducer Array for Cross-Sectional Velocity Field Reconstruction in Acoustic Tomography

As a part of industrial process tomography, acoustic tomography (AT) is a promising method for high-quality velocity field measurement, which is widely utilized in the monitoring of multiphase flow, atmospheric environment, and so on. However, subjected to the safety and reliability requirements of furnaces or industrial pipes, the number of installed transducers is restricted, which results in sparse valid data, and, thus, ill-posed and rank-deficient limited-data linear AT. Therefore, special attention should be given to the transducer arrangement in the framework construction module to maximize the reconstruction accuracy. Unlike methods that minimize relative reconstruction error including true value of interesting field, finding an alternative prior knowledge-based predictor that maps the reconstruction error is more practical and preferred. In this work, based on the prior information that the linear independence of vectors incorporates maximum measurement information, a linear independence degree (LID) metric is proposed to optimize the transducer array. It contains no prior information about flow velocity fields, which enables the procedure to be performed in an off-line mode. A covariance matrix adaptation evolution strategy (CMA-ES) algorithm is employed to solve this minimization problem. Proof-of-concept demonstrations, including representative synthetic vortical flow fields with a biased single vortex or with double vortices and experimental data-based velocity field, are performed by comparison with existing representative optimization strategies. Qualitative and quantitative results show the feasibility and robustness of the LID metric in improving imaging accuracy of arbitrary practical velocity fields based on AT. This prior metric is expected to provide guidance for better diagnosis of multiphase flow and optimization of industrial flow process.

Lipids in Atherosclerosis: Pathophysiology and the Role of Calculated Lipid Indices in Assessing Cardiovascular Risk in Patients with Hyperlipidemia

The role of lipids is essential in any phase of the atherosclerotic process, which is considered a chronic lipid-related and inflammatory condition. The traditional lipid profile (including the evaluation of total cholesterol, triglycerides, high-density lipoprotein, and low-density lipoprotein) is a well-established tool to assess the risk of atherosclerosis and as such has been widely used as a pillar of cardiovascular disease prevention and as a target of pharmacological treatments in clinical practice over the last decades. However, other non-traditional lipids have emerged as possible alternative predictors of cardiometabolic risk in addition to traditional single or panel lipids, as they better reflect the overall interaction between lipid/lipoprotein fractions. Therefore, this review deals with the lipid involvement characterizing the pathophysiology of atherosclerosis, discussing some recently proposed non-traditional lipid indices and, in the light of available knowledge, their actual potential as new additive tools to better stratify cardiovascular risk in patients with hyperlipidemia as well as possible therapeutic targets in the clinical practice.

Machine learning-based predictions of gamma passing rates for virtual specific-plan verification based on modulation maps, monitor unit profiles, and composite dose images

Machine learning (ML) methods have been implemented in radiotherapy to aid virtual specific-plan verification protocols, predicting gamma passing rates (GPR) based on calculated modulation complexity metrics because of their direct relation to dose deliverability. Nevertheless, these metrics might not comprehensively represent the modulation complexity, and automatically extracted features from alternative predictors associated with modulation complexity are needed. For this reason, three convolutional neural networks (CNN) based models were trained to predict GPR values (regression and classification), using respectively three predictors: (1) the modulation maps (MM) from the multi-leaf collimator, (2) the relative monitor units per control point profile (MUcp), and (3) the composite dose image (CDI) used for portal dosimetry, from 1024 anonymized prostate plans. The models’ performance was assessed for classification and regression by the area under the receiver operator characteristic curve (AUC_ROC) and Spearman’s correlation coefficient (r). Finally, four hybrid models were designed using all possible combinations of the three predictors. The prediction performance for the CNN-models using single predictors (MM, MUcp, and CDI) were AUC_ROC = 0.84 ± 0.03, 0.77 ± 0.07, 0.75 ± 0.04, and r = 0.6, 0.5, 0.7. Contrastingly, the hybrid models (MM + MUcp, MM + CDI, MUcp+CDI, MM + MUcp+CDI) performance were AUC_ROC = 0.94 ± 0.03, 0.85 ± 0.06, 0.89 ± 0.06, 0.91 ± 0.03, and r = 0.7, 0.5, 0.6, 0.7. The MP, MUcp, and CDI are suitable predictors for dose deliverability models implementing ML methods. Additionally, hybrid models are susceptible to improving their prediction performance, including two or more input predictors.

SARS-CoV-2 Prevalence in Malawi Based on Data from Survey of Communities and Health Workers in 5 High-Burden Districts, October 2020

To determine early COVID-19 burden in Malawi, we conducted a multistage cluster survey in 5 districts. During October-December 2020, we recruited 5,010 community members (median age 32 years, interquartile range 21-43 years) and 1,021 health facility staff (HFS) (median age 35 years, interquartile range 28-43 years). Real-time PCR-confirmed SARS-CoV-2 infection prevalence was 0.3% (95% CI 0.2%-0.5%) among community and 0.5% (95% CI 0.1%-1.2%) among HFS participants; seroprevalence was 7.8% (95% CI 6.3%-9.6%) among community and 9.7% (95% CI 6.4%-14.5%) among HFS participants. Most seropositive community (84.7%) and HFS (76.0%) participants were asymptomatic. Seroprevalence was higher among urban community (12.6% vs. 3.1%) and HFS (14.5% vs. 7.4%) than among rural community participants. Cumulative infection findings 113-fold higher from this survey than national statistics (486,771 vs. 4,319) and predominantly asymptomatic infections highlight a need to identify alternative surveillance approaches and predictors of severe disease to inform national response.

Analyzing Resident Perceptions of Current Radiation Oncology Evaluation Methods

Background In an effort to facilitate positive change in radiation oncology (RO) resident evaluations, we collect and report resident familiarity and perceptions of current evaluation methods. We hypothesize familiarity with evaluation methods is predictive of the perceived utility of evaluations, stress or intimidation of evaluations, and behavioral changes. Methods A survey with Likert-scale questions was distributed to RO residents at 13 institutions. Questions assessed resident familiarity with evaluation methods, satisfaction and utility of different aspects of evaluations, stress, or intimidation of receiving evaluations, and likelihood of changing post-evaluation. Likert-type scales included: 1) not familiar=1; extremely familiar=5; 2) extremely unsatisfied=1; extremely satisfied=5; 3) strongly disagree=1; strongly agree=5; 4) extremely unlikely=1; extremely likely=5. Summary statistics are reported as (median, IQR). Regression analysis was used to analyze relationships between different variables. Results Eight of 13 programs responded to the survey with a response rate of 50% (52/104 residents). Surveyed residents reported being "not familiar" with the Next Accreditation System (1, 1-1) and "slightly familiar" with the six Core Competencies and the factors use to assess them (2, 2-3). Satisfaction with aspects of the resident evaluations was variable: frequency (3, 3-4), timeliness (3.5, 3-4), clarity of strengths or areas of improvement (4, 3-4). Overall, residents agreed that evaluations were useful in informing them of their competence, progress, strengths, and areas of improvement (median 4). Residents were neutral when asked whether they agreed with the statement, "receiving evaluations intimidates me," although 24/52 (46%) reported either "somewhat" or "strongly" agreeing with the statement (3, 2-4). Most residents agreed that receiving evaluations was stressful (4, 2-4). Additionally, residents indicated they were "very likely" to change behaviors and practices following evaluations (4, 4-5). Resident-reported familiarity with the evaluation methods was not found to be a significant predictor of the utility of evaluation methods (coefficient= -0.02, p=0.83), stress (coefficient= -0.11, p=0.62), or intimidation of receiving evaluations (coefficient= -0.06, p=0.792), or the likelihood of changing post evaluation (coefficient= 0.41, p=0.204). Discussion Familiarity with evaluation methods is not correlated with perceptions or behavioral changes necessitating further investigation of alternative predictor variables. Despite the low familiarity with evaluation tools, most residents reported that evaluations were useful and likely to elicit changes in their behaviors and practice, highlighting the value of current evaluation methods. This study is a part of a larger multi-institutional project that aims to consolidate evaluation methods across different institutions and develop interventions to improve the training process for the RO trainees. In an effort to facilitate positive change in radiation oncology (RO) resident evaluations, we collect and report resident familiarity and perceptions of current evaluation methods. We hypothesize familiarity with evaluation methods is predictive of the perceived utility of evaluations, stress or intimidation of evaluations, and behavioral changes. A survey with Likert-scale questions was distributed to RO residents at 13 institutions. Questions assessed resident familiarity with evaluation methods, satisfaction and utility of different aspects of evaluations, stress, or intimidation of receiving evaluations, and likelihood of changing post-evaluation. Likert-type scales included: 1) not familiar=1; extremely familiar=5; 2) extremely unsatisfied=1; extremely satisfied=5; 3) strongly disagree=1; strongly agree=5; 4) extremely unlikely=1; extremely likely=5. Summary statistics are reported as (median, IQR). Regression analysis was used to analyze relationships between different variables. Eight of 13 programs responded to the survey with a response rate of 50% (52/104 residents). Surveyed residents reported being "not familiar" with the Next Accreditation System (1, 1-1) and "slightly familiar" with the six Core Competencies and the factors use to assess them (2, 2-3). Satisfaction with aspects of the resident evaluations was variable: frequency (3, 3-4), timeliness (3.5, 3-4), clarity of strengths or areas of improvement (4, 3-4). Overall, residents agreed that evaluations were useful in informing them of their competence, progress, strengths, and areas of improvement (median 4). Residents were neutral when asked whether they agreed with the statement, "receiving evaluations intimidates me," although 24/52 (46%) reported either "somewhat" or "strongly" agreeing with the statement (3, 2-4). Most residents agreed that receiving evaluations was stressful (4, 2-4). Additionally, residents indicated they were "very likely" to change behaviors and practices following evaluations (4, 4-5). Resident-reported familiarity with the evaluation methods was not found to be a significant predictor of the utility of evaluation methods (coefficient= -0.02, p=0.83), stress (coefficient= -0.11, p=0.62), or intimidation of receiving evaluations (coefficient= -0.06, p=0.792), or the likelihood of changing post evaluation (coefficient= 0.41, p=0.204). Familiarity with evaluation methods is not correlated with perceptions or behavioral changes necessitating further investigation of alternative predictor variables. Despite the low familiarity with evaluation tools, most residents reported that evaluations were useful and likely to elicit changes in their behaviors and practice, highlighting the value of current evaluation methods. This study is a part of a larger multi-institutional project that aims to consolidate evaluation methods across different institutions and develop interventions to improve the training process for the RO trainees.

Developing an artificial neural network model to predict the durability of the RC beam by machine learning approaches

The chloride diffusivity is a significant index to describe the degradation rate of concrete structures. At present, a lot of models have been proposed by previous studies to investigate the chloride diffusivity in concrete specimens. However, most of these models only consider the effect of exposure time, and erosion depth on the chloride diffusivity and ignore the uncertain factor in the environment. This may lead to a tremendous difference existed in the predicted-to-experimental ratios of all typical chloride diffusivity models. Machine learning approaches as potential alternative predictors are often used to cope with this bias. This paper adopted five contemporary ML approaches, namely, the backpropagation (BP) neural network, the decision tree (DT), the random forest (RF), the linear regression (LR), and ridge regression (RR) to explore the chloride diffusivity. Input features considered in this study are water-cement ratios, the thickness of concrete specimens, coarse aggregate fraction volume, ratios of environmental temperature to the maintenance standard temperature, and ratios of environmental humidity to the relative humidity. It was found that the backpropagation (BP) neural network is the most accurate prediction (85%) with the lowest root mean squared error. With this superiority, A study on the importance of the input parameters reveals that the volume fraction of coarse aggregate, water-cement ratios, the thickness of the protective layer, and ratios of environment humidity to the relative humidity are the most influential parameters on chloride diffusivity. In addition, a data-driven model was established to investigate the chloride diffusivity considering the effect of random factors both in material and environment. Compared with the test results, the results indicated that the prediction results are closer to the test results, which is helpful for in-depth exploring the durability of the concrete structures.

A comparative analysis of genomic and phenomic predictions of growth-related traits in 3-way coffee hybrids.

Genomic prediction has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction models that can account, in part, for this challenge. Here, we compare and contrast genomic prediction and phenomic prediction models for 3 growth-related traits, namely, leaf count, tree height, and trunk diameter, from 2 coffee 3-way hybrid populations exposed to a series of treatment-inducing environmental conditions. The models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors. This comparative analysis demonstrates that the best-performing phenomic prediction models show higher predictability than the best genomic prediction models for the considered traits and environments in the vast majority of comparisons within 3-way hybrid populations. In addition, we show that phenomic prediction models are transferrable between conditions but to a lower extent between populations and we conclude that chlorophyll a fluorescence data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.

The area under the effect curve as an efficacy determinant for anti-infectives.

Pharmacokinetic/pharmacodynamic (PK/PD) indices making use of area under the curve, maximum concentration, and the duration that in vivo drug concentration is maintained above a critical level are commonly applied to clinical dose prediction from animal efficacy experiments in the infectious disease arena. These indices make suboptimal use of the nonclinical data, and the prediction depends on the shape of the PK profiles in the animals, determined by the species‐specific absorption, distribution, metabolism, and elimination properties, and the dosing regimen used in the efficacy experiments. Motivated by the principle that efficacy is driven by pharmacology, we conducted simulations using a generalized pathogen dynamic model, to assess the properties of an alternative efficacy predictor: the area under the effect curve (AUEC), computed using in vitro PD and in vivo PK. Across a wide range of hypothetical scenarios, the AUEC consistently showed regimen‐independent strong correlation (R 2 0.76–0.98) with in vivo efficacy, superior to all other indices. These findings serve as proof of concept that AUEC should be considered in practice as a translation tool for cross‐species dose prediction. Using AUEC for clinical dose prediction could also potentially cut down animal use by reducing or avoiding dose fractionation experiments.

The prediction of financial distress probability in East Java province governments

Beginning in the mid-1970s, financial distress in local governments became big news when New York City nearly collapsed due to the energy crisis and high inflation. When local governments experience financial distress, the responsibility to provide services to the community both now and in the future cannot be fulfilled. Local governments can only maintain services to the community if they are able to avoid financial distress. If the financial distress condition is maintained, there will be a stagnation in the level of community welfare which will have an impact on regional economic growth because it is not supported by adequate public services. Some local governments like Pennsylvania, have implemented a measure to indicate financial distress as stipulated in the 1987 Financially Distressed City Act (UU 47). However, the local government in Indonesia does not yet have an absolute measure for this financial distress condition. For this reason, financial distress in this study will use a relative measure indicated by the probability value measured using a defined approach. The results of the study provide an overview of how the relevant values in local government financial statements are able to predict financial distress in local governments. The findings show that the variables intergovernmental revenue (IGR), financial independence (FIND), employee expenditure (EMPEX), capital expenditure (CAPEX), and operating position (OPPOS) have a significant effect and can be used as an alternative predictor of financial distress with more comprehensive suitability with the financial characteristics of local government in East Java Province.

The Role of Trans Cranial Doppler Ultrasonography as an Alternative Predictor for Hemodynamic Significance of Persistent Ductus Arteriosus in Preterm Neonates

The Role of Trans Cranial Doppler Ultrasonography as an Alternative Predictor for Hemodynamic Significance of Persistent Ductus Arteriosus in Preterm Neonates

The Role of Trans Cranial Doppler Ultrasonography as an Alternative Predictor for Hemodynamic Significance of Persistent Ductus Arteriosus in Preterm Neonates

The Role of Trans Cranial Doppler Ultrasonography as an Alternative Predictor for Hemodynamic Significance of Persistent Ductus Arteriosus in Preterm Neonates

Prognostic Value of Oral Epstein-Barr Virus DNA Load in Locoregionally Advanced Nasopharyngeal Carcinoma.

Background: Plasma Epstein–Barr virus (EBV) DNA load has been widely used for nasopharyngeal carcinoma (NPC) prognostic risk stratification. However, oral EBV DNA load, a non-invasive biomarker that reflects the EBV lytic replication activity, has not been evaluated for its prognostic value in NPC yet. Methods: A total number of 1,194 locoregionally advanced NPC (LA-NPC) patients from south China were included from a prospective observational cohort (GARTC) with a median follow-up of 107.3 months. Pretreatment or mid-treatment mouthwashes were collected for EBV DNA detection by quantitative polymerase chain reaction (qPCR). The difference of pre- and mid-treatment oral EBV DNA load was tested by the Wilcoxon signed-rank test. The associations of oral EBV DNA load with overall survival (OS), progression-free survival (PFS), distant metastasis–free survival (DMFS), and locoregional relapse-free survival (LRFS) were assessed using the log-rank test and multivariate Cox regression. Results: The high level of the oral EBV DNA load (>2,100 copies/mL) was independently associated with worse OS (HR = 1.45, 95% CI: 1.20–1.74, p < 0.001), PFS (HR = 1.38, 95% CI: 1.16–1.65, p < 0.001), DMFS (HR = 1.66, 95% CI: 1.25–2.21, p = 0.001), and LRFS (HR = 1.43, 95% CI: 1.05–1.96, p = 0.023). Similar and robust associations between oral EBV DNA load and prognosis were observed for patients in both the pretreatment and mid-treatment stages. The detection rate (71.7 vs. 48.6%, p < 0.001) and the median load of oral EBV DNA (13,368 vs. 382 copies/mL, p < 0.001) for patients in the pretreatment stage were significantly higher than those in the mid-treatment stage. The combination of the oral EBV DNA load and TNM staging provided a more precise risk stratification for the LA-NPC patients. Conclusion: Oral EBV DNA load was an alternative non-invasive predictor of prognosis and may facilitate risk stratification for the LA-NPC patients.

What About an Alternative Chain-Ladder Predictor for the Ultimate Claims Amount?

What About an Alternative Chain-Ladder Predictor for the Ultimate Claims Amount?

Formal and informal learning as deterrents of turnover intentions: Evidence from frontline workers during a crisis

AbstractEmployee learning and development is critical to organizational success, particularly during times of crisis when increased volatility and demand necessitate adaptability and skill. We draw on theories of workplace learning and social exchange to investigate the unique influence of formal training, informal learning, and organizational support for training on the turnover intentions of frontline workers battling COVID‐19. Survey data were gathered from a sample of Direct Support Professionals (N = 481) in New York state who continued their work supporting individuals with developmental and intellectual disabilities during the global coronavirus pandemic. The results support our predictions that employee engagement in formal training and, to a lesser extent, informal learning, as well as individuals’ perceptions of organizational support for training, explain unique variance in turnover intentions. Alternative predictors, including tenure and region, explained some unique variance in turnover intentions, but neither age nor learning goal orientation demonstrated meaningful effects. Relative weights analysis revealed that the strongest negative predictors of turnover intentions were organizational support for training and engagement in formal training. Altogether these findings identify possible methods of deterring turnover intentions, by highlighting the key role that organizations play in supporting continuous learning amongst their employees, even in times of crisis. Implications of this study are discussed for research and practice on workforce learning and development for frontline workers and others, more generally.

Voice parameters for difficult mask ventilation evaluation: an observational study.

BackgroundMask ventilation (MV) is an essential component of airway management. Difficult mask ventilation (DMV) is a major cause for perioperative hypoxic brain injury; however, predicting DMV remains a challenge. This study aimed to determine the potential value of voice parameters as novel predictors of DMV in patients scheduled for general anesthesia.MethodsWe included 1,160 adult patients scheduled for elective surgery under general anesthesia. The clinical variables usually reported as predictors of DMV were collected before surgery. Voice sample of phonemes ([a], [o], [e], [i], [u], [ü], [ci], [qi], [chi], [le], [ke], and [en]) were recorded and their formants (f1–f4) and bandwidths (bw1-bw4) were extracted. The definition of DMV was the inability of an unassisted anesthesiologist to ensure adequate ventilation during MV under general anesthesia. Univariate and multivariate logistic regression analyses were used to explore the association between voice parameters and DMV. The predictive value of the voice parameters was evaluated by assessment of area under the curve (AUC) of receiver operating characteristic (ROC) curves of a stepwise forward model.ResultsThe prevalence of DMV was 218/1,160 (18.8%). The AUC of the stepwise forward model (including o_f4, e_bw2, i_f3, u_pitch, u_f1, u_f4, ü_bw4, ci_f1, qi_f1, qi_f4, qi_bw4, chi_f1, chi_bw2, chi_bw4, le_pitch, le_bw3, ke_bw2, en_pitch, and en_f2, en_bw4) attained a value of 0.779. The sensitivity and specificity of the model were 75.0% and 71.0%, respectively.ConclusionsVoice parameters may be considered as alternative predictors of DMV, but additional studies are needed to confirm the initial findings.