Predictors Of Success Research Articles

Sacral neuromodulation in nursing home residents: Predictors of success and complications in a national cohort of older adults.

There is limited evidence to support the efficacy of sacral neuromodulation (SNM) for older adults with overactive bladder (OAB). This study aims to report outcomes following SNM among nursing home (NH) residents, a vulnerable population with high rates of frailty and comorbidity. This is a retrospective cohort study of long-stay NH residents who underwent a trial of percutaneous nerve evaluation (PNE) or Stage 1 permanent lead placement (Stage 1) between 2014 and 2016. Residents were identified using the Minimum Data Set linked to Medicare claims. The primary outcome of this study was successful progression from trial to implant. Rates of 1-year device explant/revisions were also investigated. Trial of SNM was observed in 1089 residents (mean age: 77.9 years). PNE was performed in 66.9% of residents and 33.2% underwent Stage 1. Of Stage 1 procedures, 23.8% were performed with simultaneous device implant (single-stage). Overall, 53.1% of PNEs and 72.4% of Stage 1 progressed to device implant, which was associated with Stage 1 procedure versus PNE (adjusted relative risk [aRR]: 1.34; 95% confidence interval [95% CI]: 1.21-1.49) and female versus male sex (aRR: 1.26; 95% CI: 1.09-1.46). One-year explant/revision was observed in 9.3% of residents (6.3% for PNE, 10.5% for Stage 1, 20.3% single-stage). Single stage procedure versus PNE was significantly associated with device explant/revision (aRR: 3.4; 95% CI: 1.9-6.2). In this large cohort of NH residents, outcomes following SNM were similar to previous reports of younger healthier cohorts. Surgeons managing older patients with OAB should use caution when selecting patients for single stage SNM procedures.

Grit and Graduation: Exploring Persistence Among Urban University Students

This study examines predictors of college success in 968 students, focusing on the influence of grit, demographic, and academic factors, using logistic and Cox regression analyses. It finds that higher grit scores significantly predict increased graduation rates and decreased dropout rates, with gender, ACT scores, entry-level, and summer course enrollment as key factors. The research highlights the importance of considering broader socioeconomic and systemic contexts that affect the development of resilience and grit. It emphasizes that resilience and grit are not only personal attributes but also outcomes shaped by socioeconomic status and systemic barriers, such as income disparities and institutional biases. The study advocates for educational environments that support individual resilience and grit while addressing socio-economic and systemic inequalities to promote equitable student success. This approach calls for integrated supportive structures and policies within educational institutions to alleviate the compounded challenges faced by students, ensuring resilience and grit are nurtured within a context that recognizes and confronts systemic inequity.

Enhancing Spatial Ability among Undergraduate First-Year Engineering and Architecture Students

Spatial ability (SA) refers to the ability to generate, retain and manipulate abstract visual images in one’s head. Research of this ability conducted during the last few decades had shown that SA is an essential ability to the development of mathematical skills in children. Later studies addressed adult SA development and accordingly found that SA was a predictor of success in Science, Technology, Engineering and Mathematics (STEM) fields of academic studies. Recently, focus has also been directed to another related field which is architecture. This group has not been extensively researched in relation to its spatial thinking abilities. The current research examines the efficiency of a new training program aimed at improving engineering and architecture student’s SA abilities. This program was based on previous intervention programs but was innovative in its combination of both traditional and computer-based teaching methods and combining frontal learning, hands on model building and augmented reality (AR) tools, all in a short program of 20 h. We asked whether the training program will be effective in improving student’s SA ability and whether this improvement will remain across different factors such as gender, faculty and math level. The results showed a significant improvement in SA abilities among the students who took the training program, compared with the control group. These results were true for both engineering and architecture students. Moreover, the results showed the training program to be efficient regardless of gender or prior math level.

Response times are affected by mispredictions in a stochastic game

Acting as a goalkeeper in a video-game, a participant is asked to predict the successive choices of the penalty taker. The sequence of choices of the penalty taker is generated by a stochastic chain with memory of variable length. It has been conjectured that the probability distribution of the response times is a function of the specific sequence of past choices governing the algorithm used by the penalty taker to make his choice at each step. We found empirical evidence that besides this dependence, the distribution of the response times depends also on the success or failure of the previous prediction made by the participant. Moreover, we found statistical evidence that this dependence propagates up to two steps forward after the prediction failure.

Optimisation-based strategies to identification of material parameters of hygro-thermo-mechanical problems

Applications involving hygro-thermo-mechanical models are found in engineering fields as diverse as electronic packaging, concrete structures, composite materials and wood structures. Within the framework of wood processing, hygro-thermo-mechanical problems account for the cross-dependence of moisture content, temperature and dimensional changes of wooden components. Numerical simulation plays an important role in predicting the behaviour of wooden structures and, therefore, use of appropriate process and material parameters is essential for a successful prediction. The present work is inserted in this context and addresses application of optimisation techniques to identification of heat transfer, moisture diffusion and moisture-dependent swelling/shrinkage parameters. The direct problem is formulated based on a fully coupled transient solution of the energy, moisture and momentum transfer conservation laws. A global–local hybrid optimisation technique is proposed combining swarm intelligence and deterministic approaches, respectively based on particle swarm optimisation and the Nelder–Mead (NM) technique. The first stage aims at reducing the search space, so that the NM algorithm is able to determine the global minimum with acceptable accuracy. The strategy is both robust and efficient, being able to avoid local minima with reduced number of fitness computations. An illustrative example featuring a moisture-gaining process is also discussed, in which special attention is placed on the elastic and material swelling coupled effects caused by moisture transfer.

Invasion risk of the currently cultivated alien flora in southern Africa is predicted to decline under climate change

Alien species can have massive impacts on native biodiversity, ecosystem functioning, and human livelihoods. Assessing which species from currently cultivated alien floras may escape into the wild and naturalize is essential for efficient and proactive ecosystem management and biodiversity conservation. Climate change has already promoted the naturalization of many alien plants in temperate regions, but whether it is similar in (sub)tropical areas is insufficiently known. In this study, we used species distribution models for 1527 cultivated alien plants to evaluate current and future invasion risks across different biomes and 10 countries in southern Africa. Our results confirm that the area of suitable climate is a strong predictor of naturalization success among the cultivated alien flora. In contrast to previous findings from temperate regions, however, climatic suitability is generally predicted to decrease for potential aliens across our (sub)tropical study region. While increasingly hotter and drier conditions are likely to drive declines in suitability for potential aliens across most biomes of southern Africa, in some the number of potential invaders is predicted to increase under moderate climate change scenarios (e.g. in dry broadleaf forests and flooded grasslands). We found that climatic suitability is expected to decline less for aliens originating from continents with the tropical biome or from the Southern Hemisphere. In addition, we found that the climatically suitable area will decline less for aliens that have already naturalized in the region. While the number of potential invaders may decrease across southern Africa under future climate change, our results suggest that already naturalized aliens will continue to threaten native species and ecosystems.

Uncovering Success Patterns in Track Cycling: Integrating Performance Data with Coaches and Athletes’ Perspectives

Track cycling entails a challenging progression from the youth categories to elite competition. Hence, this study aimed to investigate the importance of early performance and various publicly available performance indicators in predicting the success of male and female cyclists across different track disciplines. Additionally, the study enriches the findings by incorporating interviews with international-level coaches and athletes. A retrospective analysis of data from UCI track cycling databases was conducted, supplemented by interviews with international-level coaches and athletes. The success rate for highly ranked junior track cyclists was found to be less than 20%, with a majority of these athletes specializing in sprint events, regardless of gender. The study indicated that the UCI ranking and points earned during the season were not reliable indicators for distinguishing future success (p < 0.05). From the interviews, we identified three main themes: (1) trends in career success from the youth to elite categories, (2) performance markers as predictors of future success, and (3) the challenges and time involved in reaching elite categories. Junior category performance alone may not be the sole indicator of future success in track cycling. However, integrating performance analyses with practitioners and athletes’ perspectives enables a deeper understanding of the results and the developmental context.

Successful prediction of left bundle branch block-induced cardiomyopathy and treatment effect by artificial intelligence-enabled electrocardiogram.

Left bundle branch block (LBBB) induced cardiomyopathy is an increasingly recognized disease entity. However, no clinical testing has been shown to be able to predict such an occurrence. A 70-year-old male with a prior history of LBBB with preserved ejection fraction (EF) and no other known cardiovascular conditions presented with presyncope, high-grade AV block, and heart failure with reduced EF (36%). His coronary angiogram was negative for any obstructive disease. No other known etiologies for cardiomyopathy were identified. Artificial intelligence-enabled ECGs performed 6 years prior to clinical presentation consistently predicted a high probability (up to 91%) of low EF. The patient successfully underwent left bundle branch area (LBBA) pacing with correction of the underlying LBBB. Subsequent AI ECGs showed a large drop in the probability of low EF immediately after LBBA pacing to 47% and then to 3% 2 months post procedure. His heart failure symptoms markedly improved and EF normalized to 54% at the same time. Artificial intelligence-enabled ECGS may help identify patients who are at risk of developing LBBB-induced cardiomyopathy and predict the response to LBBA pacing.

Mechanistic Modeling of In Vitro Biopharmaceutic Data for a Weak Acid Drug: A Pathway Towards Deriving Fundamental Parameters for Physiologically Based Biopharmaceutic Modeling.

Mechanistic modeling of in vitro experiments using metabolic enzyme systems enables the extrapolation of metabolic clearance for in vitro-in vivo predictions. This is particularly important for successful clearance predictions using physiologically based pharmacokinetic (PBPK) modeling. The concept of mechanistic modeling can also be extended to biopharmaceutics, where in vitro data is used to predict the in vivo pharmacokinetic profile of the drug. This approach further allows for the identification of parameters that are critical for oral drug absorption in vivo. However, the routine use of this analysis approach has been hindered by the lack of an integrated analysis workflow. The objective of this tutorial is to (1) review processes and parameters contributing to oral drug absorption in increasing levels of complexity, (2) outline a general physiologically based biopharmaceutic modeling workflow for weak acids, and (3) illustrate the outlined concepts via an ibuprofen (i.e., a weak, poorly soluble acid) case example in order to provide practical guidance on how to integrate biopharmaceutic and physiological data to better understand oral drug absorption. In the future, we plan to explore the usefulness of this tutorial/roadmap to inform the development of PBPK models for BCS 2 weak bases, by expanding the stepwise modeling approach to accommodate more intricate scenarios, including the presence of diprotic basic compounds and acidifying agents within the formulation.

Agile meets quantum: a novel genetic algorithm model for predicting the success of quantum software development project

Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts. Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored. This study investigates key causes of challenges that could hinders the adoption of traditional agile approaches in quantum software projects and develop an Agile-Quantum Software Project Success Prediction Model (AQSSPM). Firstly, we identified 19 causes of challenging factors discussed in our previous study, which are potentially impacting agile-quantum project success. Secondly, a survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Naive Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM. Utilizing GA with NBC, project success probability improved from 53.17 to 99.68%, with cost reductions from 0.463 to 0.403%. Similarly, GA with LR increased success rates from 55.52 to 98.99%, and costs decreased from 0.496 to 0.409% after 100 iterations. Both methods result showed a strong positive correlation (rs = 0.955) in causes ranking, with no significant difference between them (t = 1.195, p = 0.240 > 0.05). The AQSSPM highlights critical focus areas for efficiently and successfully implementing agile-quantum projects considering the cost factor of a particular project.

Reducing Airway Occlusion Time Without Losing Accuracy to Predict Successful Mechanical Ventilator Liberation During the Measurement of the Timed Inspiratory Effort Index.

In 2013, a new predictor of successful mechanical ventilation liberation named timed inspiratory effort (TIE) index was devised with the normalization of the maximum inspiratory pressure (obtained within 60 s of unidirectional airway occlusion) with the time at which the value was reached. The aim of this study was to verify whether the presence of a sequence of a certain number of inspiratory effort values between 30-60 s > 1.0 cm H2O/s could predict weaning success in a performance comparable to the TIE index. This was a retrospective observational study using 4 databases of previous studies on the TIE index. All patients receiving mechanical ventilation for ≤ 24 h were eligible. Liberation from mechanical ventilation-extubation decisions was made based on performance with spontaneous breathing trials. P < .05 was considered significant. The performance of the TIE index was evaluated by calculating the area under the receiver operating characteristics (AUROC) curve. From 349 eligible patients, 165 subjects were selected for analysis. The AUROC for the TIE index in the studied sample was 0.92 (95% CI 0.87-0.97, P < .001). A sequence of ≤ 4 inspiratory efforts > 1.0 cm H2O/s was found in 51.5% of the subjects, with successful ventilatory liberation occurring in 95.3%. The highest specificity values belonged to the sequence of ≤ 4 and ≤ 5 inspiratory efforts > 1.0 cm H2O/s; the highest positive predictive value and positive likelihood ratio belonged to the sequence of ≤ 4 inspiratory efforts > 1.0 cm H2O/s. The mean time that could have been spared if the procedure were interrupted after the first sequence of 4 inspiratory efforts > 1.0 cm H2O/s was 23 ± 3 s. The presence of a sequence of ≤ 4 inspiratory efforts > 1.0 cm H2O/s during the TIE index measurement was a reliable predictor of weaning success, which could allow timely interruption of the procedure and entail a substantial reduction in airway occlusion time.

Variation coefficient of stone density and renal cortical thickness: the parameters evaluating non-contrast computed tomography imaging for predict extracorporeal shock wave lithotripsy success.

The stone density (SD) is not the same in all parts of the stone due to the heterogeneous nature of the stone and the shock wave (SW) passes through tissues of many different densities until it reaches the stone. These factors affect the success of Extracorporeal Shock Wave Lithotripsy (ESWL). We aimed to evaluate the effect of the Variation Coefficient of Stone Density (VCSD) and Renal Cortical Tickness (RCT) on the success of ESWL. Between 2020 and 2023, 510 patients who underwent ESWL were divided into 2 groups treatment success (n:304) and treatment failure (n:206). Non-Contrast Computed Tomography (NCCT) imaging values of hydronephrosis degree of the kidney, stone location, stone volume (SV), stone-skin distance (SSD), SD, Standard deviation of Stone Density (SDSD), VCSD, RCT, Soft-Tissue Thickness (STT), Muscle Thickness (MT) were analyzed. VCSD value was obtained by dividing SDSD by SD. Along the SW, tissues were divided into three components: kidney (renal cortex), muscle and other soft tissues. RCT, MT and SSD were measured at three different angles (0°, 45°, and 90°) and these 3 lengths were averaged. In univariate analysis, Body Mass Index (BMI), SV, SD, VCSD, SSD, RCT and STT were demonstrated to affect ESWL success. In multivariate analysis, low BMI, SV, SD, RCT and large VCSD were significant independent predictors of ESWL success. Among these parameters, VCSD had the highest prediction accuracy, followed by SD, SV, RCT and BMI, respectively. This study demonstrated that VCSD value and RCT are predictive parameters in determining the treatment of patients with urinary calculi and selecting suitable ESWL candidates.

Predictors of Success of Endonasal Septal Perforation Repair: A 10-Year Experience.

Background: There is no consensus on optimal repair technique for nasal septal perforations (NSPs). Objective: To measure success rate and evaluate predictors of success for NSP repair. Methods: Medical records of patients who underwent NSP repair from 2010 to 2020 were reviewed. Included patients had at least 60 days of postsurgical follow-up. Surgical technique involves an endonasal approach; subperichondrial dissection with local flap mobilization; and multilayer closure using cartilage interposition graft, fascia graft, and mucoperichondrial flaps. A chi-squared test or Fisher exact test was used for statistical analysis. Results: Eighty-one repairs were performed with a closure rate of 86%. The median patient age was 46 years (range 13-77); 34.6% of perforations were ≥2 cm. Conchal (77.8%), rib (7.4%), or septal (7.4%) cartilage was used as graft material. A complication rate of 8.6% was reported. Perforation size or graft material had no impact on successful closure rate. Of patients with failed repairs, 55% had perioperative complications or conditions associated with poor healing. Conclusion: An endonasal approach for NSP repair showed a high success rate across diverse presentations; however, NSP repair was significantly more likely to be successful in patients without perioperative complications or pre-existing conditions associated with poor wound healing.

Functional and structural connectivity success predictors of real-time fMRI neurofeedback targeting DLPFC: Contributions from central executive, salience, and default mode networks.

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF), a training method for the self-regulation of brain activity, has shown promising results as a neurorehabilitation tool, depending on the ability of the patient to succeed in neuromodulation. This study explores connectivity-based structural and functional success predictors in an NF n-back working memory paradigm targeting the dorsolateral prefrontal cortex (DLPFC). We established as the NF success metric the linear trend on the ability to modulate the target region during NF runs and performed a linear regression model considering structural and functional connectivity (intrinsic and seed-based) metrics. We found a positive correlation between NF success and the default mode network (DMN) intrinsic functional connectivity and a negative correlation with the DLPFC-precuneus connectivity during the 2-back condition, indicating that success is associated with larger uncoupling between DMN and the executive network. Regarding structural connectivity, the salience network emerges as the main contributor to success. Both functional and structural classification models showed good performance with 77% and 86% accuracy, respectively. Dynamic switching between DMN, salience network and central executive network seems to be the key for neurofeedback success, independently indicated by functional connectivity on the localizer run and structural connectivity data.

759 Revisiting the ETV Success Score Using Machine Learning Can We Do Better?

INTRODUCTION: The endoscopic third ventriculostomy (ETV) success score (ETVSS) is a useful decision-making heuristic when considering the probability of surgical success, defined as no repeat CSF diversion surgery needed within six months. Nonetheless, the original dataset published in 2009 was relatively small (n = 618) and logistic regression (LR) model performance modest. METHODS: We queried the MarketScan national database for the years 2005-2020 to identify patients &lt;18 years of age who underwent a first-time ETV and subsequently had at least 6 months of continuous enrollment in the database. We collected data on predictors matching the original ETVSS—age, etiology of hydrocephalus, and history of any previous shunt placement. Next, we used six ML algorithms—LR, support vector classifier, random forest, k-nearest neighbors, XGBoost, and Naive Bayes—to develop predictive models. Finally, we used nested cross-validation to assess models’ comparative performances on unseen data. RESULTS: We identified 1698 patients that met inclusion criteria, and 1139 (67%) had successful ETVs. Performance of all models exceeded that of the original ETVSS, with the LR model still performing best with area under the receiver operating characteristic curve (AUROC) 0.90 (95% confidence interval, 0.86-0.94), sensitivity 0.79 (0.70-0.88), and specificity 0.91 (0.87-0.95) at Youden’s index. The random forest was the second-best ML algorithm following closely behind. This compares favorably to the original ETVSS with AUROC 0.80 (0.76-0.84, p &lt; 0.01), sensitivity 0.65 (0.47-0.85, p &lt; 0.01), and specificity 0.71 (0.65-0.75, p &lt; 0.01). CONCLUSIONS: Our predictions for ETV success outperform the current gold standard. Logistic regression may still be the most interpretable and predictive algorithm; further work is underway to externally validate these models.

Impact of previous surgery on success of magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) to treat pediatric epilepsy: An institutional experience.

There is an emerging role for minimally invasive magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) in the treatment of pediatric epilepsy refractory to medication. To date, predictors of MRgLITT success have not been established in a sizeable singular experience. Correspondingly the aim of this study was to elucidate if previous surgical history predicts MRgLITT success in this setting. A retrospective review was conducted of our MRgLITT procedures for pediatric (aged <19 years) epilepsy from 2011-2020 with documented seizure outcomes at 1- and 2-years after procedure. Categorical and continuous data were compared using Chi-squared and student's t-test respectively. A total of 41 patients satisfied all criteria with 16 (39%) females and 25 (61%) males. Following MRgLITT, seizure-freedom at 1-year was achieved in 15 (37%) patients. In the cohort, there were 14 (34%) patients who had undergone previous open surgery for epilepsy at mean age of 9.4 ± 5.5 years. Patients with a previous open surgery history were found to statistically experience longer length of hospitalization after MRgLITT (P=0.04) with a statistically lower proportion of seizure-freedom at 1-year after MRgLITT (14% vs 48%, P=0.03). However, there was no difference in the rate of seizure-freedom at 2-years (29% vs 41%, P=0.44), as well as no difference in subsequent surgical interventions for seizure management between groups. Based on our institutional experience, patients with previous open surgery history may experience longer length of hospitalization after MRgLITT for pediatric epilepsy and lesser response in seizure-freedom within the first year but with non-inferior seizure-freedom by the second year.

Neurocognitive Predictors of Emotion Regulation Success in Mid-to Late-Life Depression

Neurocognitive Predictors of Emotion Regulation Success in Mid-to Late-Life Depression

178 Pace and Pitch: Predictive Factors for Seed Funding and Development

OBJECTIVES/GOALS: Securing seed funding and external support can be a daunting process. Institutions are increasingly looks for quantitative assurance of impact and accountability. This study investigates factors predictive of seed funding selection, including pace of submissions as well as external support. METHODS/STUDY POPULATION: Using Generalized Logistic Mixed Models (GLMMs), we model factors found to be predictive of researcher success, and model demographic factors as well, to understand the complex interplay of researcher background, professional networks and preparation, and researcher persistence. The following factors were modeled as potentially predictive of researcher success: faculty rank; co-PI; h-index; rate of application; prior award funding amounts; and research-focused social media posts. RESULTS/ANTICIPATED RESULTS: After effects are finalized, we expect that pace of seed fund applications and the strength co-PIs, as measured by h-indices, to be significant predictors of researcher success for both securing seed funding and external support. DISCUSSION/SIGNIFICANCE: This study identifies features associated with eventual research program success and can be used to support accountability and impact efforts at an institutional level. Research institutes strive to ensure equal access to these opportunities and train applicants to produce improved project proposals. Results from this study inform these efforts.

Hyperspectral imaging predicts free fatty acid levels, peroxide values, and linoleic acid and oleic acid concentrations in tree nut kernels

Imaging technologies are advancing rapidly in food processing systems to reduce food waste. However, modelling techniques, sample sizes and dataset proportioning methods significantly affect the performance of models in predicting food quality variables. This study examined the potential of hyperspectral imaging to predict peroxide values (PV), free fatty acid (FFA) levels and fatty acid concentrations, including oleic and linoleic acid, in two tree nuts, canarium and macadamia. The effectiveness of artificial neural network (ANN) regression and partial least squares regression (PLSR) techniques to make these predictions was examined. Additionally, the importance of the dataset size on prediction accuracy and the dataset-proportioning method for developing predictive models were assessed. Both ANN and PLSR models predicted FFA levels and oleic acid concentrations with high accuracy, but PV and linoleic acid concentrations were predicted poorly. Changing the test-dataset proportioning method for the small dataset led to comparable R2test values by both ANN and PLSR in predicting FFA levels. Successful prediction of FFA levels could be explained partly by high variability, even in the dataset with the small number of samples. The study highlights the significance of factors like dataset size, test-dataset proportioning method, and model selection when predicting the quality attributes.

Machine learning technique in the north zagros earthquake prediction

Studying the changes in seismicity, and the potential of the occurrences of large earthquakes in a seismic zone is not only extremely important from the aspect of seismological research, but it is additionally significant in the decisions of crisis management. Since, nowadays Machine learning techniques have proven the high ability for analyzing information, and discovering the relations among the parameters, in this research were tested some of these techniques for the earthquake prediction. For analysis, the north Zagros seismic catalogue was selected. A region that is an active seismic zone, and large cities are located there. Moreover, nine seismic parameters were used to study the possibility of large earthquake prediction for 1 month using three different Machine Learning (ML) techniques (Artificial Neural Network (ANN), Random Forest, and Support Vector Machine (SVM)). The accuracy of prediction models was evaluated using four different statistical measures (recall, accuracy, precision, and F1-score). The results showed that the (ANN) method is more accurate than other methods. Based on three investigated methodologies, greater accuracy results have been produced to forecast the earthquakes with bigger scale earthquakes about the completeness of the seismic catalogue in large magnitude. These achievements promise the possibility of successful prediction in a short period, which is hopeful for better crisis management performance.