Excess Rate Research Articles

Paradoxical improvement in exercise tolerance and peak VO2 consumption after treatment with ivabradine and beta-blockers in a patient with mild dilated cardiomyopathy and inappropriate sinus tachycardia – A case report

Abstract Background Left bundle branch block (LBBB) is a rare conduction disorder in athletes associated with ventricular dyssynchrony, which can lead to left ventricular systolic dysfunction and exercise intolerance. Inappropriate sinus tachycardia (IST) is characterized by an excessive heart rate that is not related to physiological needs, often resulting in reduced exercise capacity. Managing these conditions in athletes can be challenging, as standard treatments like beta-blockers and ivabradine, while effective in controlling heart rate, are described to be associated with a reduction in maximal exercise performance. Case Summary A 50-year-old amateur athlete presented with exercise intolerance, LBBB, and mild dilated cardiomyopathy due to ventricular dyssynchrony. Resting ECG and 24-hour monitoring confirmed IST. Initial cardiopulmonary exercise testing (CPET) off-therapy showed a rapid heart rate increase during exertion, an early plateau in oxygen pulse, and reduced peak VO2 (22.1 ml/kg/min, 76% of the predicted value). After one month of ivabradine 5 mg bid, there was some improvement in these parameters. At the third follow-up, during combined therapy of ivabradine (5 mg bid) and metoprolol (50 mg bid), the heart rate response during exercise normalized, and CPET parameters significantly improved, with peak VO2 reaching 29.2 ml/kg/min (101% of the predicted value). Discussion This case highlights a paradoxical improvement in exercise tolerance and peak VO2 with combined ivabradine and beta-blocker therapy in a patient with IST. The treatment optimized the heart rate response during exercise, suggesting that individualized strategies can enhance exercise performance in patients with IST and mild cardiomyopathy, despite the expected limitations of these medications.

Study on the Impact of Corporate Training on the Employees Willingness to Leave

Human resources are know for their fluidity nature. The phenomenon of excessive departure rates within enterprises brings numerous adverse consequences. It is indeed a significant issue that poses a disturbance to the long-term development of businesses. In addition, organizational identity plays a crucial role as it can enhance the sense of affiliation and loyalty that employees hold towards the organization, thereby making them more willing to stay. Organizational culture, being a soft force of the enterprise, is capable of creating an excellent working environment. It enhances employees' identification and sense of belonging to the organization, boosts their job satisfaction, and have an important impact on reducing the tendency to leave. Moreover, the theory of organizational integration can improve employees' sense of belonging and loyalty to the organization, ultimately making them more inclined to remain within it.

Theoretical and numerical bifurcation analysis in a prey–predator model with prey harvesting effort

This paper explores the influence of harvest efforts on a discrete-time prey–predator model. We established the stability criteria for fixed points in the proposed model. We investigate codim-1 and codim-2 bifurcations, exploring a range of multiple and generic bifurcations within the discrete model. The model demonstrates fold, flip, and Neimark–Sacker bifurcations, as well as resonances such as 1:2, 1:3, and 1:4 at distinct fixed points. Through the application of the critical normal form theorem and bifurcation theory, we calculate normal form coefficients for each bifurcation. Our results show that an excessive prey harvest rate can actually destabilize the model dynamics. Numerical simulations validate our findings, shedding light on the intricate dynamics of the system.

Case Analysis of Tencent’s Cross-Border Merger and Acquisition of Sumo Group

As the process of global economic integration continues to accelerate, more and more companies are beginning to implement global development strategies, and cross-border M&A are an important way to achieve it. This paper takes Tencent's cross-border M&A of Sumo Group as an example. It first introduces the two parties and the process of the M&A, then analyzes the motivations of the M&A from multiple aspects, and uses the event study method and financial indicator method to analyze the performance of the M&A. The research results show that the final value of the cumulative excess rate of return in the ten trading days before and after the M&A date on July 19, 2021 is positive, and multiple financial indicators have performed well in the long term, indicating that Tencent has achieved positive market performance and financial performance after the M&A. Based on the analysis and conclusion, this paper enriches the literature on cross-border M&A in the game industry, and also has certain implications for cross-border M&A of other game companies.

Experimental and Numerical Simulation of Flow Modes in Flow Focusing/Blurring Nozzle

The flow mode of the flow focusing/blurring nozzle was studied through experimental and numerical simulation methods. The experimental results indicate that the flow mode of the nozzle can be classified into flow focusing, transition, and flow blurring based on the location of the liquid jet breakup. Numerical simulation studies have found that the transformation of flow mode is mainly related to viscous shear force, gas pressure on the jet surface, and liquid inertia force. The increase in the gas flow rate changes the flow mode by affecting the viscous shear force. The increase in the liquid flow rate changes the flow mode by affecting the liquid inertial force. When the liquid flow rate is high, the increase in the liquid flow rate affects the flow mode by affecting the gas pressure on the jet surface. The study also found that excessive gas flow rates can hinder the flow of liquid inside the nozzle, while excessive liquid flow rates can hinder the breakup of liquid inside the nozzle. Therefore, the normal operation of the flow focusing/blurring nozzle requires ensuring that the gas and liquid flow rates are within the normal range.

Kinetic and Thermodynamic Study of Ag+, Cu2+, and Zn2+ Ion Adsorption on LTA for High-Performance Antibacterial Coating

Antibacterial powder coatings have attracted increasing attention with the awakening of people’s health awareness. Silver antibacterial agent has been widely used in coating system due to its superior stability and durability. However, silver ions have the problems of excessive release rate and the tendency to cause yellowing of the coating film. The addition of Cu2+ and Zn2+ can effectively alleviate these two phenomena. In this paper, the ternary exchange kinetics of Ag+, Cu2+, and Zn2+ were studied to provide a theoretical basis for the synthesis of LTA-Ag-Cu-Zn. The reaction kinetics study shows that the selectivity and the adsorption capacity of LTA to Ag+ is higher than that of Cu2+ and Zn2+. The thermodynamic analysis discovers that LTA has the highest selectivity for Ag+, and the exchange between the two is spontaneous. In contrast, the selectivity of LTA to Cu2+ and Zn2+ is concentration-dependent. By establishing the three-ion competitive adsorption curve, it is found that the selectivity of Ag+ is the highest, and the selectivity of copper and zinc is similar. These trends result from Ag+ ions’ low hydration energy, small hydration radius, and strong electronegativity. This research lays the groundwork for developing high-performance LTA-Ag-Cu-Zn tri-ion exchange antibacterial agents.

Source specific fine particles and rates of asthma and COPD healthcare encounters pre- and post-implementation of the Tier 3 vehicle emissions control regulations

We examined associations between seven source-specific PM2.5 concentrations and rates of asthma and COPD hospitalizations and emergency department (ED) visits in New York State and compared the changes in excess rates (ERs) between pre- (2014-2016) and post-implementation (2017-2019) of the Tier 3 automobile emission controls on new vehicles policy. A modified time-stratified case-crossover design and conditional logistic regression were employed to estimate the ERs of asthma and COPD hospitalizations and ED visits associated with interquartile range (IQR) increases in source-specific PM2.5 concentrations. The seven PM2.5 sources were spark-ignition emissions (GAS), diesel (DIE), biomass burning (BB), road dust (RD), secondary nitrate (SN), secondary sulfate (SS), and pyrolyzed organic rich (OP). Residual PM2.5 (PM2.5 – specific source [e.g., GAS]), daily temperature, relative humidity, weekday, and holidays were included in the model. IQR increases in GAS, SS, RD, BB, and SN were associated with increased ERs of asthma ED visits (highest ERs: 0.5%-3.1%), while a negative association was observed with DIE and OP. The rate of asthma hospitalizations was associated with increased RD concentrations (ERs: 1.3%-1.7%). Both COPD ED visit and hospitalization rates were associated with increased OP (ERs: 2.1%-3.4%), and increased SS was positively associated with COPD ED visits (ER = 3.8%). In summary, after Tier 3 implementation (2017-2019), we found lower ERs for COPD admissions associated with BB, RD, SN, and SS compared to 2014-2016. However, rates of asthma ED visits associated with source-specific PM2.5 concentrations were generally higher for all sources, except DIE, post- versus pre-implementation, requiring further research for validation.

Controlled migration of oleic acid amide through adsorption on TS-1 zeolite for long-lasting anti-adhesive rubber composites

The adhesion of materials on the surface can affect the appearance, durability, and performance stability of rubber composites. Incorporating anti-adhesive additives into composites is an effective strategy to improve anti-adhesive properties, as these additives migrate to the surface to form an anti-adhesive layer. However, excessive migration rates can result in poor durability of the anti-adhesive layer and significant powder adhesion on the surface. This study prepared titanium silicate zeolite adsorbed with acid amide composite filler (OAA@TS-1) by adsorbing OAA onto the active sites of TS-1, which was then incorporated into natural rubber (NR). The adsorption effect reduces the migration rate of OAA within the rubber composite, allowing more OAA to remain in the matrix. Additionally, the adsorption is reversible, and the adsorbed OAA can overcome the adsorption through molecular thermal motion, which can migrate to the surface. After the anti-adhesive layer is worn, OAA can persistently migrate outward, effectively repairing the layer and enhancing the durability of the anti-adhesive properties. In comparison to rubber composites filled solely with OAA, the OAA@(TS-1) filled rubber composite exhibits a remarkable reduction of 55% in the amount of adherent powder on the composite surface and a 24% decrease in the coefficient of friction after three friction cycles. After slicing and leaving for 3 days, the contact angle increases by 18%. The long-lasting anti-adhesive rubber composite developed in this study demonstrates significant application potential in areas such as material conveyance and power transmission.

Promises vs. Profits: The long-term impact of performance commitments in M&As

ABSTRACT This study utilizes data from Chinese listed companies to investigate the relationship between performance commitments in mergers and acquisitions (M&A) and long-term excess returns. Our results indicate that entering into performance commitment contracts during the M&A process can significantly boost long-term excess return rates one year after M&A. However, the effect becomes negative after three years of M&A. Mechanism analysis shows that performance commitments enhance managerial short-sightedness and negatively impact perceived long-term company performance.

Enhancing Operational Efficiency in Crushers Through the Use of an Industry 4.0 Based Crusher Control System

In the aggregate and mining industry, an excessive flow rate of raw material from the feeder, caused by irregularities in the raw material being processed by crushers, can lead to blockages or excessive strain on the crusher. Conversely, a low flow rate of raw material can result in high energy consumption by the crusher, despite operating at a low capacity. The issues encountered in the first group result in excessive energy usage in the secondary and tertiary groups. The study focuses on a system that utilizes artificial intelligence and is based on industry 4.0 principles. The system aims to maintain production in the crusher within a specific range by controlling the flow rates of the feeders using an algorithm. This control is done automatically without the need for user intervention. The system optimizes energy consumption while maximizing production capacity and ensures uninterrupted operation. The system was developed during the installation phase at an aggregate pilot plant in the Kahramanmaraş Evri region. It assesses the material capacity data using a belt scale on the crusher, feeder, and output conveyor. This data is then compared to the limit values stored in the database, and the system generates an information signal to initiate the required control actions. Based on this matching result, it sends information to the inverter, coordinates the production cycle, manages and documents the process stages using a structured learning system and artificial intelligence logic.The installation procedure was conducted using two distinct density gradation inputs. As a consequence of the reporting, the records in the report were compared during both the active and inactive states of the system. The project achieved an efficiency of 22% in terms of energy consumption per unit capacity. Based on the whole yearly energy usage, a total of 368609.7 kg of carbon emissions were averted. The facility's aggregate crushing capacity was increased by 40%.

Faktor Perubahan Alih Fungsi Lahan Pertanian Akibat Pembangunan Kawasan Aerotropolis Kulon Progo dan Dampaknya Terhadap Tata Guna Lahan LP2B, Pemukiman dan Pariwisata

The development of the Aerotropolis area in Kulon Progo Regency causes frequent changes in agricultural land use to residential areas, hotels, restaurants, and other infrastructure. The excessive rate of conversion of agricultural land will have a negative impact on Kulon Progo Regency. Therefore, it is necessary to know the factors causing changes in agricultural land use and their impact on LP2B areas, residences, and tourism. The research method used is descriptive-analytical with statistical data; observations of situational conditions and the results of interviews regarding the problem being studied will be analyzed and then explained more specifically and in depth. The results of the analysis show that the phenomenon of land conversion in Kulon Regency is influenced by internal factors, namely the strategic location of agricultural land and its proximity to public facilities. The productivity of the agricultural sector has decreased due to difficult farmer regeneration, which causes economic factors to emerge. Apart from that, it is also influenced by external factors, namely increasing population growth, increasing development of the trade and services sector, the COVID-19 pandemic, and government policies that facilitate the conversion of agricultural land. The impact is that land and agricultural productivity are decreasing while the area of residential areas and the tourism sector are increasing. As a result, the GDP growth rate in the agricultural sector decreased, while the accommodation and food and drink (tourism) sector and the service sector increased. This trend has the potential to reduce the implementation of LP2B in Kulon Progo Regency. Recommendations that need to be made are strengthening government policies regarding the protection of LP2B and the involvement of many parties from the community, private sector, NGOs (Non-Government Organizations), and local governments regarding the implementation of LP2B

Implementation of Electronic Medical Record Interventions to Maximize Opioid Disposal after Surgery in Routine Clinical Practice.

Despite guideline directed opioid prescribing after surgery, many patients retain excess opioids. Leftover pills increase the risk for misuse, diversion, and dependency. It is unclear whether interventions applied in routine clinical practice can increase excess opioid disposal rates. All adult patients at our institution with postoperative opioid prescriptions were identified in general surgery, otolaryngology, plastic surgery, neurosurgery, urology and transplant surgery. Disposal rates for 453 pre-intervention patients (surgery between 3/1/23-6/30/23) were compared with 545 intervention patients (surgery between 10/1/23-1/31/24). Pre- and post-intervention patients were called 1-3 months after surgery and asked about excess opioids.Interventions:1. Opioid disposal information sheets provided after surgery.2. Opioid disposal reminder delivered via patient portal in electronic medical record (EMR).3. Best practice alert (BPA) triggered in EMR at post operative visit reminded providers to discuss opioid disposal. Opioid disposal outcomes were obtained from 70% (699/998) of patients. 54% (375/699) had excess opioids. The percentage of patients disposing of their excess opioids increased from 30% (39/130) pre-intervention to 83% (204/245) post-intervention, p<0.00001. Forty-seven percent (247/545) of post-intervention patients had discussions with their providers after a BPA fired at their post-operative visits. Patients who had a provider discussion were more likely to dispose of excess opioids than those who only received an information sheet and patient portal reminder (92% vs 70%, p < 0.00001). Interventions easily incorporated into the EMR as part of routine clinical practice can markedly increase excess opioid disposal rates after surgery. When information on opioid disposal and patient portal reminders are combined with disposal discussions with providers at the postoperative visit, opioid disposal rates of >90% can be achieved.

Generalization Guarantees of Gradient Descent for Shallow Neural Networks

Abstract Significant progress has been made recently in understanding the generalization of neural networks (NNs) trained by gradient descent (GD) using the algorithmic stability approach. However, most of the existing research has focused on one-hidden-layer NNs and has not addressed the impact of different network scaling. Here, network scaling corresponds to the normalization of the layers. In this article, we greatly extend the previous work (Lei et al., 2022; Richards &amp; Kuzborskij, 2021) by conducting a comprehensive stability and generalization analysis of GD for two-layer and three-layer NNs. For two-layer NNs, our results are established under general network scaling, relaxing previous conditions. In the case of three-layer NNs, our technical contribution lies in demonstrating its nearly co-coercive property by utilizing a novel induction strategy that thoroughly explores the effects of overparameterization. As a direct application of our general findings, we derive the excess risk rate of O(1/n) for GD in both two-layer and three-layer NNs. This sheds light on sufficient or necessary conditions for underparameterized and overparameterized NNs trained by GD to attain the desired risk rate of O(1/n). Moreover, we demonstrate that as the scaling factor increases or the network complexity decreases, less overparameterization is required for GD to achieve the desired error rates. Additionally, under a low-noise condition, we obtain a fast risk rate of O(1/n) for GD in both two-layer and three-layer NNs.

Minus the Error: Estimating d N / d S and Testing for Natural Selection in the Presence of Residual Alignment Errors.

Errors in multiple sequence alignments (MSAs) are known to bias many comparative evolutionary methods. In the context of natural selection analyses, specifically codon evolutionary models, excessive rates of false positives result. A characteristic signature of error-driven findings is unrealistically high estimates of dN/dS (e.g., >100), affecting only a small fraction (e.g., ∼0.1%) of the alignment. Despite the widespread use of codon models to assess alignment quality, their potential for error correction remains unexplored. We present BUSTED-E: a novel method designed to detect positive selection while concurrently identifying alignment errors. This method is a straightforward adaptation of the BUSTED flexible branch-site random effects model used to fit distributions of dN/dS, with an important modification: it integrates an "error-sink" component representing an abiological evolutionary regime (dN/dS > 100), and provides the option for masking errors in the MSA for downstream analyses. Statistical performance of BUSTED-E on data simulated without errors shows that there is a small loss of power, which can be mitigated by model averaged inference. Using four published empirical datasets, we show BUSTED-E reduces unrealistic rates of positive selection detection, often by an order of magnitude, and improves biological interpretability of results. BUSTED-E also detects errors that are largely distinct from other popular alignment cleaning tools (HMMCleaner and BMGE). Overall, BUSTED-E is a robust and scalable solution for improving the accuracy of evolutionary analyses in the presence of residual alignment errors, contributing to a more nuanced understanding of natural selection and adaptive evolution.

Association of gestational weight gain rate with infant birth weight and cesarean delivery: A prospective cohort study in Nepal.

Gestational weight gain (GWG) is linked to pregnancy outcomes, such as birth weight and delivery mode, though research in low-income countries like Nepal is limited. We examined the association of GWG rate with infant birth weight and cesarean delivery in a prospective cohort of 191 pregnant women in Nepal, using data collected from August 2018 to August 2019 at a peri-urban hospital in Dhulikhel. Participants included women with singleton, full-term live births, with GWG rate calculated from weight gain between the second and late pregnancy stages, divided by the weeks in between. GWG rate categories-adequate, inadequate, or excessive-were defined by pre-pregnancy Body Mass Index (BMI) specific to GWG recommendations from the 2009 Institute of Medicine report. Ethical approval was obtained from Institutional Review Board of Kathmandu University and Rutgers University. Statistical analyses in SPSS and Stata revealed that 52.4% of mothers exceeded the recommended GWG rate, particularly among overweight and obese women (0.4 ± 0.2 kg/week and 0.5 ± 0.2 kg/week, respectively). The average birth weight was 2964.9 ± 407.0 grams, with 12% of infants classified as low-birth-weight. Cesarean delivery was recorded in 45% of the women. After controlling for factors like age, education, ethnicity, occupation, parity and BMI, each 1 kg/week increase in GWG from the second to third trimester correlated with a 392-gram increase in birth weight (β = 391.9, 95%CI = 67.2-716.7, p = 0.01), while excessive GWG rate led to a 148-gram increase over adequate GWG rate (β = 148.1, 95%CI = 8.7-287.5, p = 0.03). However, GWG rate was not significantly linked to cesarean delivery. These findings suggest that maternal GWG rate positively affects infant birth weight but not cesarean delivery, underscoring the need for larger studies to explore GWG rate's effects on maternal and neonatal outcomes.

Mortality from type 2 diabetes mellitus across municipalities in Mexico

BackgroundOne in six Mexican adults’ lives with type 2 diabetes mellitus (T2DM), which is the third leading cause of death in the country. Analyzing the geographic distribution of T2DM mortality helps identify regions with higher mortality rates. This study aimed to examine the spatial patterns of mortality from type 2 diabetes mellitus (T2DM) across municipalities in Mexico and to analyze the main contextual factors linked to this cause of death in 2020.MethodsWe employed a spatial Bayesian hierarchical regression model to estimate the risk and probability of death from type 2 diabetes mellitus (T2DM) across Mexico’s municipalities.ResultsThe SMR results revealed geographic and age-specific patterns. Central Mexico and the Yucatán Peninsula exhibited the highest excess mortality rates. For the population under 50 years of age, municipalities in Oaxaca had the highest T2DM mortality rates, whereas those aged 50 years old and older had the highest rates in Tlaxcala and Puebla. Socioeconomic factors such as low levels of educational attainment, lack of health services, dietary deficiency, and marginalization were positively associated with increased T2DM mortality risk. By contrast, GDP per capita showed a negative association. High-risk areas for T2DM mortality were prominent along the south of the Pacific Coast, the Bajío, Central Mexico, and southern Yucatán for those under 50, and along a central strip extending to the Yucatán Peninsula for the older population. Significant uncertainties in mortality risk were identified, with Central Mexico, Oaxaca, Chiapas, and Tabasco showing high probabilities of excess risk for those under 50 years of age and extended risk areas along the Gulf of Mexico for those 50 years old and older.ConclusionsThe assessment and identification of spatial distribution patterns associated with T2DM mortality, and its main contextual factors, are crucial for informing effective public health policies aimed at reducing the impact of this chronic disease in Mexico.

COVID-19 and sepsis-related excess mortality in the US during the first three years: A national-wide time series study.

The COVID-19 pandemic's global impact has been devastating, causing millions of deaths. Our study investigates excess sepsis-related mortality trends over three years during the pandemic. Using CDC's National Vital Statistics System data from January 2018 to March 2023, we projected sepsis-related deaths during the pandemic using a Poisson log-linear regression model. We compared observed versus predicted deaths and analyzed temporal trends by demographics and regions. Among the 753,160 deaths documented between March 2020 and March 2023, a significant downward trend was noted in sepsis-related mortality rates from March 2022 to March 2023, coinciding with the surge of the Omicron variant. The excess mortality rates were 170.6 per million persons (95% CI: 168.2-172.6), 167.5 per million persons (95% CI: 163.6-170.9), and 73.3 per million persons (95% CI: 69.4-76.6) in the first, second, and third years, respectively. Increased sepsis-related mortality was observed across all age subgroups, with the greatest increase noted in those aged 85 years and above compared to middle- and young-aged decedents. Disparities were also observed across racial/ethnic, sex/gender subgroups, and geographic regions. This study highlights the effectiveness of current policies and prevention measures in response to the long-term circulating of SARS-CoV-2 in the community.

Thermodynamic and Kinetic Studies on the Pyrophoricity of Uranium Flakes

The objective of this work was to arrive at quantitative estimates of pyrophoricity of uranium in different geometries. The major source of heat generation was identified to be exothermic chemical reactions of uranium with moist air. Time evolution of temperature was studied for uranium considering 3D conduction and convection heat loss routes using Finite Element Analysis. Auto ignition temperature for uranium flakes was quantified using heat balance equation and was found to be around 500 K. Excess heat generation rates needed to cause auto ignition of uranium flakes at room temperature were also estimated. The major sources of excess heat leading to its ambient ignition could be attributed to friction and other exothermic chemical reactions of process impurities in uranium.

Excess mortality associated with chikungunya epidemic in Southeast Brazil, 2023

The chikungunya virus (CHIKV) was first detected in Brazil in 2014 and has since caused major epidemics. In 2023, a major chikungunya epidemic occurred in Minas Gerais, one of the most populous states in the country. We analyzed the North and Northeast Health Macroregions of Minas Gerais, with 2.5 million inhabitants. The study used epidemiological data from the Ministry of Health’s laboratory surveillance, disease notification and mortality systems. A robust Poisson regression model was constructed to estimate expected monthly mortality. Excess deaths were calculated by comparing observed deaths with model estimates during the epidemic period. During the epidemic, there were 890 excess deaths attributed to chikungunya, translating into a mortality rate of 35.1/100,000 inhabitants. The excess mortality rate was significantly 60 times higher than the deaths reported by surveillance, with only 15 confirmed deaths. The correlation between excess deaths and laboratory-confirmed chikungunya cases was strong, while the correlation with dengue and COVID-19 was not statistically significant. The results highlighted the serious underestimation of chikungunya mortality by epidemiological surveillance. During the same year, only 420 chikungunya deaths were reported by all PAHO member countries. Routine epidemiological surveillance systems cannot capture the full impact of this disease. Excess mortality is a key measure for understanding the impact of epidemics, as demonstrated by COVID-19 and influenza pandemics. The study highlights the need for complementary tools to traditional surveillance to better assess impacts on morbidity and mortality and support priority setting in public health interventions.

A machine learning framework to adjust for learning effects in medical device safety evaluation.

Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning curves, and time-varying covariates, such as physician experience. To address these limitations, we sought to develop a machine learning (ML) framework to detect and adjust for operator learning effects. A gradient-boosted decision tree ML method was used to analyze synthetic datasets that replicate the complexity of clinical scenarios involving high-risk medical devices. We designed this process to detect learning effects using a risk-adjusted cumulative sum method, quantify the excess adverse event rate attributable to operator inexperience, and adjust for these alongside patient factors in evaluating device safety signals. To maintain integrity, we employed blinding between data generation and analysis teams. Synthetic data used underlying distributions and patient feature correlations based on clinical data from the Department of Veterans Affairs between 2005 and 2012. We generated 2494 synthetic datasets with widely varying characteristics including number of patient features, operators and institutions, and the operator learning form. Each dataset contained a hypothetical study device, Device B, and a reference device, Device A. We evaluated accuracy in identifying learning effects and identifying and estimating the strength of the device safety signal. Our approach also evaluated different clinically relevant thresholds for safety signal detection. Our framework accurately identified the presence or absence of learning effects in 93.6% of datasets and correctly determined device safety signals in 93.4% of cases. The estimated device odds ratios' 95% confidence intervals were accurately aligned with the specified ratios in 94.7% of datasets. In contrast, a comparative model excluding operator learning effects significantly underperformed in detecting device signals and in accuracy. Notably, our framework achieved 100% specificity for clinically relevant safety signal thresholds, although sensitivity varied with the threshold applied. A machine learning framework, tailored for the complexities of post-market device evaluation, may provide superior performance compared to standard parametric techniques when operator learning is present. Demonstrating the capacity of ML to overcome complex evaluative challenges, our framework addresses the limitations of traditional statistical methods in current post-market surveillance processes. By offering a reliable means to detect and adjust for learning effects, it may significantly improve medical device safety evaluation.