Predictors Of Success Research Articles

Predictors of outcomes after gastric peroral endoscopic myotomy for refractory gastroparesis: a systematic review

Background: Gastroparesis is a debilitating gastroduodenal disorder for which gastric peroral endoscopic myotomy (GPOEM) has emerged as an efficacious treatment option. However, response to GPOEM varies between 50-80%, such that preoperative predictors of treatment success are needed to guide patient selection. Methods: We performed a systematic review to identify predictors of clinical and functional response to GPOEM among adult patients with gastroparesis (PROSPERO: CRD42023457359). MEDLINE, Embase, and CENTRAL databases were searched systematically for studies reporting outcomes after GPOEM in September 2023. A narrative synthesis of predictive factors on univariable and multivariable analysis was performed with consideration of response rates via meta-analysis, and evaluation of discrimination if prognostic models were developed. ROBINS-E was used for risk of bias assessment. Results: Of 1899 articles reviewed, 30 were included. The GPOEM response rate was 63.1% (95% confidence interval 56.3%-69.5%) with most studies defining clinical success on the basis of improvement in gastroparesis cardinal symptom index (87%, 26/30). Older age, shorter duration of gastroparesis, non-diabetic etiology, lower body mass index, and response to intrapyloric botulinum toxin were associated with positive response to GPOEM on multivariable analyses. Predictors on physiological tests such as EndoFLIP or gastric emptying scintigraphy were inconsistent. No prognostic models underwent external validation. Conclusion: Currently, there are limited reproducible predictors of response to GPOEM among patients with refractory gastroparesis. Robust prospective studies investigating scalable, reproducible, and actionable biomarkers of treatment response are required.

Random forest models reveal academic and financial factors outweigh demographics in predicting completion of a year-round veterinary program

Abstract OBJECTIVE The purpose of this study was to develop random forest classifier models (a type of supervised machine learning algorithm) that could (1) predict students who will or will not complete the DVM degree requirements and (2) identify the top predictors for academic success and completion of the DVM degree. METHODS The study utilized Ross University School of Veterinary Medicine student records from 2013 to 2022. Twenty-four variables encompassing demographic (eg, age, race), academic (eg, grade point average), and financial aid (eg, outstanding balances) data were assessed in 11 cross-validated random forest machine learning models. One model was built assessing all years of data and 10 individual models were developed for each enrollment year to compare how the top predictors of success varied among the years. RESULTS Consistently, only academic and financial factors were identified as being features of importance (predictors) in all models. Demographic factors such as race were not important for predicting student success. All models performed very well to excellently based on multiple performance metrics including accuracy, ranging from 96.1% to 99%, and the areas under the receiver operating characteristic curves, ranging from 98.1% to 99.9%. CONCLUSIONS The random forest algorithm is a powerful machine learning prediction model that performs well with veterinary student academic records and is customizable such that variables important to each veterinary school’s student population can be assessed. CLINICAL RELEVANCE Identifying predictors of success as well as at-risk students is essential for providing targeted curricular interventions to increase retention and achieve timely completion of a DVM degree.

Amplitude spectrum area is dependent on the electrocardiogram magnitude: evaluation of different normalization approaches

Abstract Objective: Amplitude Spectrum Area (AMSA) of the electrocardiogram (ECG) waveform during ventricular fibrillation (VF) has shown promise as a predictor of defibrillation success during cardiopulmonary resuscitation (CPR). However, AMSA relies on the magnitude of the ECG waveform, raising concerns about reproducibility across different settings that may introduce magnitude bias. This study aimed to evaluate different AMSA normalization approaches and their impact on removing bias while preserving predictive value. Approach: ECG were recorded in 118 piglets (1-2 months old) during a model of asphyxia-associated VF cardiac arrest and CPR. An initial subset (91/118) was recorded using one device (Device 1), and the remaining piglets were recorded in the second device (Device 2). Raw AMSA and three ECG magnitude metrics were estimated to assess magnitude-related bias between devices. Five AMSA normalization approaches were assessed for their ability to remove detected bias and to classify defibrillation success. Main results: Device 2 showed significantly lower ECG magnitude and raw AMSA compared to Device 1. CPR-based AMSA normalization approaches mitigated device-associated bias. Raw AMSA normalized by the average AMSA in the 1st minute of CPR (AMSA1m-cpr) exhibited the best sensitivity and specificity for classification of successful and unsuccessful defibrillation. While the optimal AMSA1m-cpr thresholds for balanced sensitivity and specificity were consistent across both devices, the optimal raw AMSA thresholds varied between the two devices. The area under the receiver operating characteristic curve for AMSA1m-cpr did not significantly differ from raw AMSA for both devices (Device 1: 0.74 vs. 0.88, P=0.14; Device 2: 0.56 vs. 0.59, P=0.81). Significance: Unlike raw AMSA, AMSA1m-cpr demonstrated consistent results across different devices while maintaining predictive value for defibrillation success. This consistency has important implications for the widespread use of AMSA and the development of future guidelines on optimal AMSA thresholds for successful defibrillation.

CPAP delivered via a helmet interface in lightly sedated patients with moderate to severe ARDS: predictors of success outside the ICU

Objective: This study aimed to describe the outcomes and explore predictors of intubation and mortality in patients with ARDS due to COVID-19 treated with CPAP delivered via a helmet interface and light sedation. Methods: This was a retrospective cohort study involving patients with COVID-19-related ARDS who received CPAP using a helmet developed in Brazil (ELMOT), associated with a light sedation protocol in a pulmonology ward. Demographic, clinical, imaging, and laboratory data, as well as the duration and response to the ELMO-CPAP sessions, were analyzed. Results: The sample comprised 180 patients. The intubation avoidance rate was 72.8%. The lack of necessity for intubation was positively correlated with younger age, > 24-h continuous HELMET-CPAP use in the first session, < 75% pulmonary involvement on CT, and ROX index > 4.88 in the second hour. The overall in-hospital mortality rate was 18.9%, whereas those in the nonintubated and intubated groups were 3.0% and 61.2%, respectively. Advanced age increased the mortality risk by 2.8 times, escalating to 13 times post-intubation. Conclusions: ELMO-CPAP with light sedation in a pulmonology ward was successful in > 70% of patients with moderate to severe ARDS due to COVID-19. Younger age, pulmonary involvement, ROX index, and prolonged first Helmet-CPAP session duration were associated with no need for intubation. Older age and intubation are associated with mortality.

Abstract 4117438: Machine Learning Predicts Successful Transcatheter Mitral Valve Edge to Edge Repair: A Systematic Review and Meta-Analysis

Introduction: Transcatheter Mitral Valve Edge to Edge Repair (TEER) is an established percutaneous treatment for patients with severe symptomatic Mitral Regurgitation (MR). The current AHA/ACC guidelines recommend TEER for inoperable patients with severe primary MR or patients with symptomatic severe secondary MR despite medical therapy. Machine learning (ML) has emerged as a tool for TEER risk stratification due to the paucity of established risk scores. To address the lack of consensus on its efficacy, we conducted a systematic review and meta-analysis of studies that utilized ML to predict the success of TEER. Methods: Electronic databases, including Embase, MEDLINE, and the Cochrane Library, were searched from inception through April 2024. We included studies that used TEER and employed at least one ML model to predict the success of TEER. The Area Under the Receiver Operating Characteristic Curve (AUC) was used to measure the accuracy of ML risk stratification algorithms. Results: 102 publications were screened, with seven eventually included in this analysis. Two studies employed clustering techniques, two utilized extreme gradient boosting, and three used multiple ML algorithms to predict outcomes. Of the four studies that compared the accuracy of ML with traditional Cox regression, all four demonstrated higher accuracy with ML, and this difference was statistically significant in three of the four studies. The mean AUC of the aggregated ML data was 0.737 [95% CI: 0.717, 0.758], compared to 0.627 [95% CI: 0.600, 0.653] for the pooled traditional methods. Conclusions: To our knowledge, we conducted the first systematic review and meta-analysis of ML methods for prediction of TEER success. ML outperformed established risk scores, demonstrating promising potential. Future ML models, trained on larger patient datasets, may further improve predictive accuracy in this patient population.

Abstract 4136869: Time Course and Predictors of PVC Recurrence After Acutely Successful Catheter Ablation

Introduction: In prior studies, predictors of long-term success following acutely successful radiofrequency (RF) ablation have included female-sex, single-PVC morphology and EGM-QRS > 24ms. Time course and predictors of PVC recurrence following acutely successful ablation has been less well documented. Use of extended ambulatory monitoring in the immediate post-procedural period can help to identify those with early recurrence. Goals: To define time to recurrence and predictors of recurrence of PVCs after RF ablation following acutely successful ablation. Methods: Consecutive patients from January 1 st , 2016 through December 31 st , 2023 undergoing PVC ablation with available post-ablation extended ambulatory monitoring (Zio, iRhythm, San Francisco, CA) were retrospectively analyzed. Recurrence was defined as an increase in PVC burden to >5% during monitoring after acute success or failure to have sustained reduction in PVC burden of >80%. Acute success was defined as elimination of the targeted PVC after the last ablation lesion. Long-term succes was defined as absence of targeted PVC's during all follow-up visits and PVC-burden < 5% on follow-up monitoring. Results: Among 121 patients who underwent successful PVC ablation, 101 (83.4%) had long-term success and 20 (17%) had recurrence. Patients with recurrence were older (62.4 ± 12 vs 55 ± 13., p = 0.019), had a higher incidence of CAD (13 (13%) vs 9 (45%), p = 0.001) and CHF (40 (40%) vs 15 (75%), p = 0.004). At time of ablation, the recurrence group was also more likely to have >1 PVC morphology (6 (6%) vs 8 (40%), p = <0.001). Location and earliest activation time of the predominant PVC were not assocated with likelihood of recurrence. Within the recurrence group, 13 (65%) had recurrence in the immediate 2 week post-procedural period with an average time to recurrence of 3.9 days. Conclusion: Recurrence occured in slightly less than 20% of cases with a relatively high proportion of those occuring within the immediate 2 week post-procedural period during extended ambulatory monitoring. The presence of greater than 1 PVC morphology at time of ablation is a risk factor for recurrence suggesting a higher likelihood of acute suppression without durable resolution in those instances.

Abstract 4134483: How succesfully can we presribe the 'four pillars' of medications for patients with heart failure with reduced LV systolic function?

Introduction: European, American and UK guidelines all endorse the prescription of the 'four pillars' of treatment (ACE/ARBs, b-blockers, MRAs and SGLT2 inhibitors) for heart failure with reduced LV systolic function (HFrEF). Many doctors believe that it is difficult to initiate and maintain patients on all these medications. We have studied 162 consecutive patients referred to our service as outpatients from primary care, subsequently diagnosed as having HFrEF, and our ability to establish them on these medications. Methods: Patients referred with potential heart failure (signs, symptoms and raised BNP) were identified as having HFrEF by echocardiography using British Society of Echocardiography (BSE) criteria. Patients attended regular outpatient appointments with a consultant cardiologist and specialist heart failure nurse and were prescribed and uptitrated all appropriate medications with any contraindications and intolerances noted. Results: Table 1 shows the number of patients sucessfully established on maximally tolerated doses of each class of medication. Reasons for not prescibing or discontinuing were: CKD 6, severe aortic stenosis 5, asthma 3, symptomatic bradycardia 5, hypotension 3, type1 diabetes 2, syncope 1, Raynauds 1, patient choice 8 and 6 patients died before all appropriate medications could be initiated. In 10 cases no clinical reason could be identified. It can be seen that a high proportion of patients were successfully established on each individual class of medication and more than two thirds of patients were able to be maintained on all four classes of therapy. Table 2 shows the breakdown of prescribing success according to the severity of LV dysfunction. It can be seen that prescribing success appears to be higher in those with the more severe LV dysfunction. Conclusions: 1. The majority of patients can be established and maintained on appropriate polypharmacy for the treatment of LV systolic dysfunction. 2. The severity of LV systolic dysfunction is not a negative predictor of success in prescribing.

Coupling Different Machine Learning and Meta-Heuristic Optimization Techniques to Generate the Snow Avalanche Susceptibility Map in the French Alps

The focus of this study is to introduce a hybrid predictive framework encompassing different meta-heuristic optimization and machine learning techniques to identify the regions susceptible to snow avalanches. To accomplish this aim, the present research sought to acquire the best-performed model among nine different hybrid scenarios encompassing three different meta-heuristics, namely particle swarm optimization (PSO), gravitational search algorithm (GSA), and Cuckoo Search (CS), and three different ML approaches, i.e., support vector classification (SVC), stochastic gradient boosting (SGB), and k-nearest neighbors (KNN), pertaining to different predictive families. According to diligent analysis performed with regard to the blinded testing set, the PSO-SGB illustrated the most satisfactory predictive performance with an accuracy of 0.815, while the precision and recall were found to be 0.824 and 0.821, respectively. The F1-score of the predictions was found to be 0.821, and the area under the receiver operating curve (AUC) was obtained to be 0.9. Despite attaining similar predictive success via the CS-SGB model, the time-efficiency analysis underscored the PSO-SGB, as the corresponding process consumed considerably less computational time compared to its counterpart. The SHapley Additive exPlanations (SHAP) implementation further informed that slope, elevation, and wind speed are the most contributing attributes to detecting snow avalanche susceptibility in the French Alps.

News Subscription Motivation: Why Audiences Pay for News

In the face of accelerated losses in advertising revenue, news organizations are increasingly interested in strategies to increase subscription sales. Previous studies have found several predictors of paywall success in obtaining and maintaining subscriptions, willingness to pay, and paying for news. But these factors have not been linked together into one clear conceptual framework. This monograph introduces a new construct, News Subscription Motivation, that theoretically links different predictors of paying for news. Mixed-method research was employed to conceptualize and operationalize this new construct. Results of in-depth interviews and online surveys revealed six underlying motivations that drive people to pay for news: support for journalism, journalism quality, triggering by the paywall, community attachment, affordability, and content utility. We conducted a confirmatory factor analysis to validate the News Subscription Motivation Scale and used multiple regressions to demonstrate statistically significant relationships between subscription motivations and both the number of news subscriptions people report paying for and the types of subscriptions people buy (local vs. national). We discuss the theoretical and practical implications of studying news subscription motivation.

Evaluating the Replicability of Social Priming Studies

To assess the replicability of social priming findings we reviewed the extant close replication attempts in the field. In total, we found 70 close replications, that replicated 49 unique findings. Ninety-four percent of the replications had effect sizes smaller than the effect they replicated and only 17% of the replications reported a significant p-value in the original direction. The strongest predictor of replication success was whether or not the replication team included at least one of the authors of the original paper. Twelve of the 18 replications with at least one original author produced a significant effect in the original direction and the meta-analytic average of these studies suggest a significant priming effect (d = 0.40, 95% CI[0.23; 0.58]). In stark contrast, none of the 52 replications by independent research teams produced a significant effect in the original direction and the meta-analytic average was virtually zero (d = 0.002, 95% CI[-0.03; 0.03]). We argue that these results have shifted the burden of proof back onto advocates of social priming. Successful replications from independent research teams will likely be required to convince sceptics that social priming exists at all.

Understanding Language Learning Motivation from a Dual Analytical Lens: Combining Activity Theory and Life Capital

AbstractSecond language (L2) motivation has long been regarded as one of the requisites for and predictors of success in L2 learning. The scholarship on L2 motivation has grown steadily over the past 60 years, yielding several theories through a range of methods. Building upon this tradition, this paper proposes a methodological contribution to the thriving L2 motivation research arena. Specifically, we suggest a new analytical approach which combines Activity Theory with the methodological lens of ‘life capital'. We illustrate this approach through an L2 learner's narrative, taken from a larger study, with a focus on their experience of learning English over thirteen years. We argue that adopting life capital as an additional analytical layer alongside Activity Theory not only allows us to tackle methodological challenges previously associated with Activity Theory, but also offers a more nuanced understanding of motivational trajectories in L2 learning.

The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines

Wind power has emerged as a crucial substitute for conventional fossil fuels. The combination of advanced technologies such as the internet of things (IoT) and machine learning (ML) has given rise to a new generation of energy systems that are intelligent, reliable, and efficient. The wind energy sector utilizes IoT devices to gather vital data, subsequently converting them into practical insights. The aforementioned information aids among others in the enhancement of wind turbine efficiency, precise anticipation of energy production, optimization of maintenance approaches, and detection of potential risks. In this context, the main goal of this work is to combine the IoT with ML in the wind energy sector by processing weather data acquired from sensors to predict wind power generation. To this end, three different regression models are evaluated. The models under comparison include Linear Regression, Random Forest, and Lasso Regression, which were evaluated using metrics such as coefficient of determination (R²), adjusted R², mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Moreover, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were taken into consideration as well. After examining a dataset from IoT devices that included weather data, the models provided substantial insights regarding their capabilities and responses to preprocessing, as well as each model’s reaction in terms of statistical performance deviation indicators. Ultimately, the data analysis and the results from metrics and criteria show that Random Forest regression is more suitable for weather condition datasets than the other two regression models. Both the advantages and shortcomings of the three regression models indicate that their integration with IoT devices will facilitate successful energy prediction.

Prognosis and ablation success in thyroid cancer: overcoming the challenges of incomplete clinical profiles.

Differentiated thyroid carcinoma (DTC) is managed by surgery followed by radioiodine (RAI) therapy in most intermediate and high-risk patients. Most nonmetastatic patients have excellent treatment responses and have long-term disease-free status. A lack of comprehensive medical services in resource-limited nation leads to attrition of critical clinical prognostication information. This study aimed to identify readily available clinical, biochemical, and histopathological parameters to predict remnant ablation success and long-term outcomes. The study included DTC patients who underwent RAI after surgery. Ablation success was determined by thyroglobulin (Tg) and whole-body radioiodine scan. Patients were followed for at least 5 years to assess biochemical incomplete response (BIR) and structural recurrence. The study included 383 patients (a mean age of 37.8 ± 12.9 years). Successful ablation was noted in 251 (65.5%). High preablative stimulated serum Tg (presTg), papillary variants, and central and lateral compartment lymph nodal metastases were associated with ablation failure. PresTg (P < 0.001) was the most significant predictor. After a 102.9 ± 34.5 months follow-up, 280 (73.1%) patients were disease-free. BIR and structural recurrence were noted in 103 and 32 patients. PresTg (8.1 ± 27.7 vs. 92.3 ± 99.9 ng/ml), ATg (112.9 ± 389.8 vs. 43.2 ± 89.8 IU/ml), papillary variant, central [109 (66.1%) vs. 56 (33.9%)], and lateral compartment [65 (63.7%) vs. 37 (36.3%) lymph nodal metastases were associated (P < 0.05) with BIR. PresTg >10.5 has a sensitivity and specificity of 86.6 and 86.0% for predicting BIR. Patients with successful remnant ablation and a presTg level <10.5 ng/ml had a low risk of long-term disease recurrence (less than 5%). This ambispective study found that successful ablation and long-term disease-free survival were achievable in a significant proportion of DTC patients. BIR (26.9%) and structural recurrence (8.4%) were not uncommon. PresTg levels emerged as a crucial predictor of ablation success and subsequent outcomes. In resource-limited regions, presTg levels and ablation failure can aid in optimizing treatment strategies and improving patient care.

Transvaginal ultrasonography-measured cervical length versus the modified Bishop score for preinduction cervical assessment at term: A randomised controlled trial

Introduction: The inducibility of the cervix for labour induction is usually determined by cervical status evaluation. The Bishop score is historically used to forecast the success of induction of labour, although it is subjective, and not reproducible. However, transvaginal ultrasound measurements of cervical length are rarely used for preinduction cervical assessment. The study compared cervical length measured via transvaginal ultrasound and the modified Bishop score for preinduction cervical assessment at term. Methods: The study involved 72 pregnant, nulliparous women for induction of labour at term. They were randomised into the transvaginal ultrasound group and the modified Bishop score group. The cervix was said to be ‘ripe’ when the transvaginal ultrasound cervical length (CL) was &lt; 28 mm or the modified Bishop score was ⩾ 6. The cervix was considered ‘unripe’ when the Bishop score was &lt; 6 or the transvaginal ultrasound was ⩾ 28 mm. Participants with ripe cervices had induction of labour with an oxytocin infusion, while those with unripe cervices had preinduction cervical ripening with misoprostol. The primary outcome measures were the mode of delivery and the total amount of prostaglandins administered for preinduction cervical ripening. Results: There was no significant difference between the two groups with regard to the mode of delivery ( p = 0.795), the total amount of prostaglandins administered for preinduction cervical ripening (105.0 ± 51.04 µg vs 111.90 ± 52.2 µg; p = 0.0671), the proportion of women who were administered prostaglandins due to an unfavourable cervix (41.7% vs 55.6%; p = 0.812), induction-to-the-active phase of the labour interval (11.00 ± 4.2 hours vs 11.82 ± 4.12 hours; p = 0.407) or the induction-delivery interval (20.15 ± 5.7 hours vs 22.66 ± 4.33 hours; p = 0.06) in both groups, respectively. Compared with those in the Bishop score group (Bishop score ⩾ 6), the induction-to-active phase of labour intervals (6.47 ± 0.77 hours vs 7.33 ± 1.21 hours, p = 0.024) and the induction-to-delivery intervals (14.97 ± 1.0 hours vs 18.39 ± 0.85 hours; p = 0.0001) in the transvaginal ultrasound group (cervical length &lt; 28 mm) were significantly shorter, respectively. Conclusion: Preinduction cervical assessment using transvaginal ultrasound (cervical length &lt; 28 mm) or the modified Bishop score is a successful predictor of the outcome of labour induction A larger multicentre studies are needed to identify optimal cervical length cutoffs and to determine if this could decrease unnecessary prostaglandin use or decrease caesarean section rate.

Factors of Weight Loss for Telemedically Supported Metabolic Syndrome Patients in a Controlled Trial

Metabolic syndrome (MetS) is a complex of interrelated risk factors, associated with several serious chronic diseases like diabetes. The goal of this study was to find dietary factors of successful weight loss for MetS outpatients. We performed a 90-day dietary intervention in a telemedically supported, pre- and post-test, controlled trial in Hungary involving 132 MetS patients; 67 were in the intervention, and 65 were in the control group. Patients in the intervention group used wireless smart devices, a dietary logger, and a lifestyle app. During the trial, we recorded the patients’ weight loss and diet composition. For analysis, t-tests were used, and the temporal trends of diet composition in the intervention group were analyzed between two sub-groups according to weight loss success. Correlation and regression models were used to find predictors of success. The intervention group achieved more weight loss, and the success in this group was linked with more consumption of raw fruits/vegetables, poultry and potato dishes, while age had a negative effect. We conclude that telemedically supported dietary coaching is an efficient alternative for interventions directed at weight loss. Future trials should investigate the therapeutic application of diets rich in raw fruits, especially apples, and vegetables, as well as poultry dishes.

Catalyzing IVF outcome prediction: exploring advanced machine learning paradigms for enhanced success rate prognostication

This study addresses the research problem of enhancing In-Vitro Fertilization (IVF) success rate prediction by integrating advanced machine learning paradigms with gynecological expertise. The methodology involves the analysis of comprehensive datasets from 2017 to 2018 and 2010–2016. Machine learning models, including Logistic Regression, Gaussian NB, SVM, MLP, KNN, and ensemble models like Random Forest, AdaBoost, Logit Boost, RUS Boost, and RSM, were employed. Key findings reveal the significance of patient demographics, infertility factors, and treatment protocols in IVF success prediction. Notably, ensemble learning methods demonstrated high accuracy, with Logit Boost achieving an accuracy of 96.35%. The implications of this research span clinical decision support, patient counseling, and data preprocessing techniques, highlighting the potential for personalized IVF treatments and continuous monitoring. The study underscores the importance of collaboration between gynecologists and data scientists to optimize IVF outcomes. Prospective studies and external validation are suggested as future directions, promising to further revolutionize fertility treatments and offer hope to couples facing infertility challenges.

Disagreement Predicts Startup Success: Evidence from Venture Competitions

This paper examines the relationship between disagreement surrounding a startup proposition and its future success. I find that the more venture competition judges disagree on the quality of a startup, the more likely the startup is to succeed, particularly when its proposition is unique. To explain this correlation, I build on the notion that (i) entrepreneurs pursue opportunities based on their subjective beliefs and (ii) common opinion alone cannot be a source of competitive advantage. Therefore, value is disproportionately created and captured by founders with unconventional ideas that spark disagreement, and potential investors should harness disagreement as a predictor of success. I leverage data from 67 venture competitions to show that the empirical implications of this theoretical framework are supported by the data, whereas alternative explanations (e.g., that judges disagree more about risky ventures) are not. Additionally, I provide insights into which evaluators tend to disagree more often (e.g., former entrepreneurs) and which aspects of a startup (e.g., business model) are most polarizing. This work has broad implications for investors and institutions that strive to evaluate the potential of startup ideas. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsc.2024.0169 .

Social Support and Expectancy as Predictors of Licensure Examination Success Among Professional Teachers

This study aimed to determine the Licensure Examination for Teachers (LET) profile, the Level of Social Support and Expectancy and their relationships and identify the dimensions that predict LET Success Rating. This quantitative study utilized descriptive-correlational design among LET Passers and Licensed Professional Teachers produced by the University of Mindanao from 2019–2023. 303 LPTs composed of 109 Elementary and 194 Secondary teachers selected via convenience and snowball sampling responded to the virtual survey. The collected data were analyzed using mean, standard deviation, spearman correlations, and multiple regressions. Results revealed that 87% of the LPTs were top-performing and had high social support and expectancy levels during their LET preparation. The correlation between social support and the LET Success Rating is positive but very weak. Likewise, the correlation between expectancy and LET Success Rating is positive but very weak. Moreover, social support predicts the LET Success Rating by 1.9%; expectancy predicts it by 0.4%. Furthermore, support from Friends and/or classmates has moderate and positive correlations and has a strong positive predictive power with LET Success Rating.

The significance of individual patient characteristics in a cohort of patients with inflammatory bowel disease treated with vedolizumab

&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Aim&amp;lt;/strong&amp;gt; Care for the inflammatory bowel disease (IBD) patients presents unique challenges as decisions regarding therapy must consider numerous distinct characteristics of each patient. The aim of the study was to recognize patients&amp;amp;rsquo; characteristics as predictors of success in vedolizumab treatment.&amp;lt;br /&amp;gt;&amp;lt;strong&amp;gt;Methods&amp;lt;/strong&amp;gt; In a retrospective observational study, data regarding age, gender, body mass index (BMI), length of disease, previous exposure to anti-tumour necrosis factor (TNF), drugs, and smoking status were extracted from the routine clinical practice. Patients were assessed for clinical remission and steroid-free remission after the 26-week treatment with vedolizumab.&amp;lt;br /&amp;gt;&amp;lt;strong&amp;gt;Results&amp;lt;/strong&amp;gt; The study included 76 patients with UC and 63 with CD. A total of 63 (out of 76;&amp;amp;nbsp;&amp;lt;br /&amp;gt;82.9%) (Cl: 72.5-90.6% ) of UC and 54 (out of 63; 85.7%) (Cl: 74.6-93.3%) CD patients achieved clinical remission in the 26-week vedolizumab treatment. Over five years, illness was noticed in 32 (53.1%) CD patients. Clinical remission was not achieved in six (out of 13; 46.1%) UC patients aged 40-49 years and six (out of nine; 66.6%) CD patients aged 30-49 years. Among CD patients, remission was achieved in 22 (85.7%) females and 23 (63.6%) males. Remission rates were generally higher in patients with a BMI of 18.6-25 and 25.1-30. Previous exposure to anti TNF drugs and smoking status did not influence treatment outcomes.&amp;lt;br /&amp;gt;&amp;lt;strong&amp;gt;Conclusion&amp;lt;/strong&amp;gt; The efficacy of vedolizumab is a viable treatment option for both ulcerative colitis and Crohn&amp;#039;s disease. The exploration of individual patient characteristics holds promise in predicting a treatment outcome.&amp;lt;/p&amp;gt;

Dynamic trigger-based attacks against next-generation IoT malware family classifiers

The evolution of IoT malware and the effectiveness of defense strategies, e.g., leveraging malware family classification, have driven the development of advanced classification learning models. These models, particularly those that utilize model-extracted features, significantly enhance classification performance while minimizing the need for extensive expert knowledge from developers. However, a critical challenge lies in the interpretability of these learning models, which can obscure potential security risks. Among these risks are backdoor attacks, a sophisticated and deceptive threat where attackers induce malicious behaviors in the model under specific triggers.In response to the growing need for integrity and reliability in these models, this work assesses the vulnerability of state-of-the-art IoT malware classification models to backdoor attacks. Given the complexities of attacking model-based classifiers, we propose a novel trigger generation framework, B-CTG, supported by a specialized training procedure. This framework enables B-CTG to dynamically poison or attack samples to achieve specific objectives. From an attacker’s perspective, the design and training of B-CTG incorporate knowledge from the IoT domain to ensure the attack’s effectiveness. We conduct experiments under two distinct knowledge assumptions: the main evaluation, which assesses the attack method’s performance when the attacker has limited control over the model training pipeline, and the transferred setting, which further explores the significance of knowledge in predicting attacks in real-world scenarios.Our in-depth analysis focuses on attack performance in specific scenarios rather than a broad examination across multiple scenarios. Results from the main evaluation demonstrate that the proposed attack strategy can achieve high success rates even with low poisoning ratios, though stability remains a concern. Additionally, the inconsistent trends in model performance suggest that designers may struggle to detect the poisoned state of a model based on its performance alone. The transferred setting highlights the critical importance of model and feature knowledge for successful attack predictions, with feature knowledge proving particularly crucial. This insight prompts further investigation into model-agnostic mitigation methods and their effectiveness against the proposed attack strategy, with findings indicating that stability remains a significant concern for both attackers and defenders.