Class-incremental learning with Balanced Embedding Discrimination Maximization.

Class incremental learning is committed to solving representation learning and classification assignments while avoiding catastrophic forgetting in scenarios where categories are increasing. In this work, a unified method named Balanced Embedding Discrimination Maximization (BEDM) is developed to make the intermediate embedding more distinctive. Specifically, we utilize an orthogonality constraint based on doubly-blocked Toeplitz matrix to minimize the correlation of convolution kernels, and an algorithm for similarity visualization is introduced. Furthermore, uneven samples and distribution shift among old and new tasks eventuate strongly biased classifiers. To mitigate the imbalance, we propose an adaptive balance weighting in softmax to compensate insufficient categories dynamically. In addition, hybrid embedding learning is introduced to preserve knowledge from old models, which involves less hyper-parameters than conventional knowledge distillation. Our proposed method outperforms the existing approaches on three mainstream benchmark datasets. Moreover, we technically visualize that our method can produce a more uniform similarity histogram and more stable spectrum. Grad-CAM and t-SNE visualizations further confirm its effectiveness. Code is available at https://github.com/wqzh/BEDM.

A dynamic attribute reduction algorithm based on relative neighborhood discernibility degree.

This paper addresses the current existence of attribute reduction algorithms for incomplete hybrid decision-making systems, including low attribute reduction efficiency, low classification accuracy and lack of consideration of unlabeled data types. To address these issues, this paper first redefines the weakly labeled relative neighborhood discernibility degree and develops a non-dynamic attribute reduction algorithm. In addition, this paper proposes an incremental update mechanism for weakly tagged relative neighborhood discernibility degree and introduces a new dynamic attribute reduction algorithm for increasing the set of objects based on it. Meanwhile, this paper also compares and analyses the improved algorithm proposed in this study with two existing attribute reduction algorithms using 8 data sets in the UCI database. The results show that the dynamic attribute reduction algorithm proposed in this paper achieves higher attribute reduction efficiency and classification accuracy, which further validates the effectiveness of the algorithm proposed in this paper.

A modified step-ramp-step protocol to prescribe constant-speed exercise in treadmill running.

PURPOSE: This study investigated whether a running-adapted version of the cycling-based "step-ramp-step" (SRS) protocol would improve prediction of V ˙ O2 in treadmill exercise compared to the traditional prescriptive approach. METHODS: Fourteen healthy individuals (6 females; 25 ± 6 years; 66.1 ± 12.7 kg) performed a treadmill-based SRS protocol including a ramp-incremental test to task failure followed by two constant-speed bouts within the moderate-(MODstep-below estimated lactate threshold; θLT), and heavy-intensity domains (HVYstep-between θLT and respiratory compensation point; RCP). Using the uncorrected V ˙ O2-to-speed relationship from the ramp exercise, three constant-speed bouts were performed at 40-50% between: baseline and θLT (CSEMOD); θLT and RCP (CSEHVY); and RCP and peak (CSESEV). For CSEMOD, CSEHVY, and CSESEV measured end-exercise V ˙ O2 was compared to predicted V ˙ O2 based on the: (i) "SRS-corrected" V ˙ O2-to-speed relationship (where MODstep and HVYstep were used to adjust the V ˙ O2 relative to speed); and (ii) linear "uncorrected" data. RESULTS: Average treadmill speeds for CSEMOD and CSEHVY were 7.8 ± 0.8 and 11.0 ± 1.4 km·h-1, respectively, eliciting end-exercise V ˙ O2 of 1979 ± 390 and 2574 ± 540 mL·min-1. End-exercise V ˙ O2 values were not different compared to SRS-predicted V ˙ O2 at CSEMOD (mean difference: 5 ± 166 mL·min-1; p = 0.912) and CSEHVY (20 ± 128 mL·min-1; p = 0.568). The linear "uncorrected" estimates were not different for CSEMOD (- 91 ± 172 mL·min-1; p = 0.068) but lower for CSEHVY (- 195 ± 146 mL·min-1; p < 0.001). For CSESEV (running speed: 13.8 ± 1.7 km·h-1), the end-exercise V ˙ O2 was not different from peak V ˙ O2 achieved during the ramp (3027 ± 682 vs. 2979 ± 655 mL·min-1; p = 0.231). CONCLUSION: In healthy individuals, the SRS protocol more accurately predicts speeds for a target V ˙ O2 compared to traditional approaches.

Medical costs and incremental medical costs of asthma among workers in the United States.

BACKGROUND: Asthma, a chronic respiratory disease, is associated with high economic burden. This study estimates per-worker medical and incremental medical costs associated with treated asthma by socioeconomic and demographic characteristics, industries, medical events, and sources of payments for workers aged ≥18 years. METHODS: We analyzed Medical Expenditure Panel Survey data from 2018 to 2020 to assess medical costs for treated asthma among workers using the International Classification of Diseases, Tenth Revision, Clinical Modification code for asthma (J45). We used two-part regression models to estimate medical and incremental medical costs controlling for covariates. All results are adjusted for inflation and presented in 2022 US dollar values. RESULTS: An estimated annual average of 8.2 million workers out of 176 million had at least one medical event associated with treated asthma. The annualized estimated per-worker incremental medical costs for those with treated asthma was $457 and was highest among: those in the age group of 35-44 years ($534), in the western region ($768), of Hispanic ethnicity ($693), employed in the utility and transportation industries ($898), males ($650), and for inpatient admissions ($754). The total annualized medical costs of treated asthma was $21 billion and total of incremental medical costs was $3.8 billion. CONCLUSION: Findings of higher incremental medical costs for treated asthma among workers in certain socioeconomic, demographic, and industry groups highlight the economic benefit of prevention and early intervention to reduce morbidity of asthma in working adults. Our results suggest that the per-person incremental medical costs of treated asthma among workers are lower than that for all US adults.

Safe Trajectory Planning for Incremental Robots Based on a Spatiotemporal Variable-Step-Size A* Algorithm.

In this paper, a planning method based on the spatiotemporal variable-step-size A* algorithm is proposed to address the problem of safe trajectory planning for incremental, wheeled, mobile robots in complex motion scenarios with multiple robots. After constructing the known conditions, the spatiotemporal variable-step-size A* algorithm is first used to perform a collision-avoiding initial spatiotemporal trajectory search, and a variable time step is utilized to ensure that the robot completes the search at the target speed. Subsequently, the trajectory is instantiated using B-spline curves in a numerical optimization considering constraints to generate the final smooth trajectory. The results of simulation tests in a field-shaped, complex, dynamic scenario show that the proposed trajectory planning method is more applicable, and the results indicate higher efficiency compared to the traditional method in the incremental robot trajectory planning problem.

Characterization and risk assessment of polycyclic aromatic hydrocarbons from the emission of different power generator.

Epileptic power supply in Sub-Saharan countries of Africa has warranted the use of power generators as an alternative source of power supply. Exhaust emission from these generators is associated with Polycyclic Aromatic Hydrocarbon (PAHs). Hence, this study focused on the determination of levels of PAHs in the emission of different brands of power generators used in Nigeria. Exhaust emissions of different power generators were sampled using a filter-sorbent sampling system with polyurethane foam (PUF) as an adsorbent material. Analysis of PAHs was carried out using a Gas Chromatograph coupled to a mass selective detector (GC- MS) operated on Electron Ionization (EI) mode. The results showed the ∑ PAHs range 14.91-26.0 µ g m - 3 . Bap was the most abundant of all the compounds with a concentration of 2.6 µ g m - 3 with a range of 2.08-3.07 µ g m - 3 . The Incremental Life Cancer Risk (ILCR) values of all the generator's emission sampled are higher than 10- 4 for both children and adult which indicate a high potential cancer risk from inhalation of emission from these generators while Hazard Quotient (HQ) values from all the power generating set in this study are all above 1 which indicated high associated non-carcinogenic. The study revealed the levels of PAHs associated with the emission of power generators in Nigeria.

Cost-effective genomic prediction of critical economic traits in sturgeons through low-coverage sequencing.

Low-coverage whole-genome sequencing (LCS) offers a cost-effective alternative for sturgeon breeding, especially given the lack of SNP chips and the high costs associated with whole-genome sequencing. In this study, the efficiency of LCS for genotype imputation and genomic prediction was assessed in 643 sequenced Russian sturgeons (∼13.68×). The results showed that using BaseVar+STITCH at a sequencing depth of 2× with a sample size larger than 300 resulted in the highest genotyping accuracy. In addition, when the sequencing depth reached 0.5× and SNP density was reduced to 50 K through linkage disequilibrium pruning, the prediction accuracy was comparable to that of whole sequencing depth. Furthermore, an incremental feature selection method has the potential to improve prediction accuracy. This study suggests that the combination of LCS and imputation can be a cost-effective strategy, contributing to the genetic improvement of economic traits and promoting genetic gains in aquaculture species.

CCSI: Continual Class-Specific Impression for data-free class incremental learning.

In real-world clinical settings, traditional deep learning-based classification methods struggle with diagnosing newly introduced disease types because they require samples from all disease classes for offline training. Class incremental learning offers a promising solution by adapting a deep network trained on specific disease classes to handle new diseases. However, catastrophic forgetting occurs, decreasing the performance of earlier classes when adapting the model to new data. Prior proposed methodologies to overcome this require perpetual storage of previous samples, posing potential practical concerns regarding privacy and storage regulations in healthcare. To this end, we propose a novel data-free class incremental learning framework that utilizes data synthesis on learned classes instead of data storage from previous classes. Our key contributions include acquiring synthetic data known as Continual Class-Specific Impression (CCSI) for previously inaccessible trained classes and presenting a methodology to effectively utilize this data for updating networks when introducing new classes. We obtain CCSI by employing data inversion over gradients of the trained classification model on previous classes starting from the mean image of each class inspired by common landmarks shared among medical images and utilizing continual normalization layers statistics as a regularizer in this pixel-wise optimization process. Subsequently, we update the network by combining the synthesized data with new class data and incorporate several losses, including an intra-domain contrastive loss to generalize the deep network trained on the synthesized data to real data, a margin loss to increase separation among previous classes and new ones, and a cosine-normalized cross-entropy loss to alleviate the adverse effects of imbalanced distributions in training data. Extensive experiments show that the proposed framework achieves state-of-the-art performance on four of the public MedMNIST datasets and in-house echocardiography cine series, with an improvement in classification accuracy of up to 51% compared to baseline data-free methods. Our code is available at https://github.com/ubc-tea/Continual-Impression-CCSI.

Comparative effectiveness of an individualized model of hemodialysis vs conventional hemodialysis: a study protocol for a multicenter randomized controlled trial (the TwoPlus trial).

BACKGROUND: Most patients starting chronic in-center hemodialysis (HD) receive conventional hemodialysis (CHD) with three sessions per week targeting specific biochemical clearance. Observational studies suggest that patients with residual kidney function can safely be treated with incremental prescriptions of HD, starting with less frequent sessions and later adjusting to thrice-weekly HD. This trial aims to show objectively that clinically matched incremental HD (CMIHD) is non-inferior to CHD in eligible patients. METHODS: An unblinded, parallel-group, randomized controlled trial will be conducted across diverse healthcare systems and dialysis organizations in the USA. Adult patients initiating chronic hemodialysis (HD) at participating centers will be screened. Eligibility criteria include receipt of fewer than 18 treatments of HD and residual kidney function defined as kidney urea clearance ≥3.5 mL/min/1.73 m2 and urine output ≥500 mL/24 h. The 1:1 randomization, stratified by site and dialysis vascular access type, assigns patients to either CMIHD (intervention group) or CHD (control group). The CMIHD group will be treated with twice-weekly HD and adjuvant pharmacologic therapy (i.e., oral loop diuretics, sodium bicarbonate, and potassium binders). The CHD group will receive thrice-weekly HD according to usual care. Throughout the study, patients undergo timed urine collection and fill out questionnaires. CMIHD will progress to thrice-weekly HD based on clinical manifestations or changes in residual kidney function. Caregivers of enrolled patients are invited to complete semi-annual questionnaires. The primary outcome is a composite of patients' all-cause death, hospitalizations, or emergency department visits at 2 years. Secondary outcomes include patient- and caregiver-reported outcomes. We aim to enroll 350 patients, which provides ≥85% power to detect an incidence rate ratio (IRR) of 0.9 between CMIHD and CHD with an IRR non-inferiority of 1.20 (α = 0.025, one-tailed test, 20% dropout rate, average of 2.06 years of HD per patient participant), and 150 caregiver participants (of enrolled patients). DISCUSSION: Our proposal challenges the status quo of HD care delivery. Our overarching hypothesis posits that CMIHD is non-inferior to CHD. If successful, the results will positively impact one of the highest-burdened patient populations and their caregivers. TRIAL REGISTRATION: Clinicaltrials.gov NCT05828823. Registered on 25 April 2023.

Framework for developing cost-effectiveness analysis threshold: the case of Egypt.

BACKGROUND: Cost-effectiveness analyses rarely offer useful insights to policy decisions unless their results are compared against a benchmark threshold. The cost-effectiveness threshold (CET) represents the maximum acceptable monetary value for achieving a unit of health gain. This study aimed to identify CET values on a global scale, provide an overview of using multiple CETs, and propose a country-specific CET framework specifically tailored for Egypt. The proposed framework aims to consider the globally identified CETs, analyze global trends, and consider the local structure of Egypt's healthcare system. METHODS: We conducted a literature review to identify CET values, with a particular focus on understanding the basis of differentiation when multiple thresholds are present. CETs of different countries were reviewed from secondary sources. Additionally, we assembled an expert panel to develop a national CET framework in Egypt and propose an initial design. This was followed by a multistakeholder workshop, bringing together representatives of different governmental bodies to vote on the threshold value and finalize the recommended framework. RESULTS: The average CET, expressed as a percentage of the gross domestic product (GDP) per capita across all countries, was 135%, with a range of 21 to 300%. Interestingly, while the absolute value of CET increased with a country's income level, the average CET/GDP per capita showed an inverse relationship. Some countries applied multiple thresholds based on disease severity or rarity. In the case of Egypt, the consensus workshop recommended a threshold ranging from one to three times the GDP per capita, taking into account the incremental relative quality-adjusted life years (QALY) gain. For orphan medicines, a CET multiplier between 1.5 and 3.0, based on the disease rarity, was recommended. A two-times multiplier was proposed for the private reimbursement threshold compared to the public threshold. CONCLUSION: The CET values in most countries appear to be closely related to the GDP per capita. Higher-income countries tend to use a lower threshold as a percentage of their GDP per capita, contrasted with lower-income countries. In Egypt, experts opted for a multiple CET framework to assess the value of health technologies in terms of reimbursement and pricing.

Incremental hemodialysis and vascular access complications: a 12-year experience in a hospital hemodialysis unit.

BACKGROUND: Incremental hemodialysis (HD) is considered a valid alternative for patients with residual kidney function. Evidence concerning its effect on vascular access is scarce. We present our 12-year experience of an incremental hemodialysis program with the aim of evaluating survival and complications of arteriovenous fistula in these patients compared to the thrice-weekly scheme. METHODS: From January 1st, 2006 to December 31st, 2017, 220 incident patients started hemodialysis, 132 (60%) of whom began hemodialysis with two sessions per week and 88 (40%) with three sessions per week. Demographic and clinical variables were assessed at the start of treatment. Data regarding arteriovenous fistula survival and complications were collected. RESULTS: Both groups had similar baseline sociodemographic and clinical characteristics. A total of 188 (85%) patients were dialyzed with an arteriovenous fistula during follow-up. Eighty-three patients had one or more fistula complications, with no differences between incremental and conventional groups (p = 0.55). Fistula survival rates showed no significant difference between the two groups, whether analyzed from the date of fistula creation (Log Rank p = 0.810) or from the date of initial fistula cannulation (Log Rank p = 0.695). CONCLUSIONS: We found no differences in arteriovenous fistula survival or complication rate between patients who started HD with an incremental versus a conventional treatment scheme. Randomized controlled clinical trials may be warranted to achieve a higher degree of evidence.

Self-balancing Incremental Broad Learning System with privacy protection.

Incremental learning algorithms have been developed as an efficient solution for fast remodeling in Broad Learning Systems (BLS) without a retraining process. Even though the structure and performance of broad learning are gradually showing superiority, private data leakage in broad learning systems is still a problem that needs to be solved. Recently, Multiparty Secure Broad Learning System (MSBLS) is proposed to allow two clients to participate training. However, privacy-preserving broad learning across multiple clients has received limited attention. In this paper, we propose a Self-Balancing Incremental Broad Learning System (SIBLS) with privacy protection by considering the effect of different data sample sizes from clients, which allows multiple clients to be involved in the incremental learning. Specifically, we design a client selection strategy to select two clients in each round by reducing the gap in the number of data samples in the incremental updating process. To ensure the security under the participation of multiple clients, we introduce a mediator in the data encryption and feature mapping process. Three classical datasets are used to validate the effectiveness of our proposed SIBLS, including MNIST, Fashion and NORB datasets. Experimental results show that our proposed SIBLS can have comparable performance with MSBLS while achieving better performance than federated learning in terms of accuracy and running time.

Cost-Effectiveness Analysis of Regorafenib versus Other Third-Line Treatments for Metastatic Colorectal Cancer.

Background: Regorafenib, a novel multikinase inhibitor, has been approved by the US Food and Drug Administration as a standard treatment choice for metastatic colorectal cancer (mCRC). Nonetheless, its substantial cost places a significant burden on social health resources and patients. However, the cost-effectiveness (CE) of regorafenib compared to other third-line therapies is still undetermined. Objective: This study aims to assess the CE of regorafenib compared to other third-line therapies for the treatment of mCRC. Methods: We conducted a comprehensive literature search in PubMed, Medline, Scopus, Embase, Cochrane Library, as well as nine other databases to identify relevant studies published up to October 2023, focusing on patients with mCRC and examining the cost-effectiveness of regorafenib. Following the screening and extraction of pertinent data, the study quality was assessed using the Quality of Health Economic Studies (QHES) checklist. Results: The literature search yielded 751 records, and after applying the inclusion criteria, 13 studies from 7 different countries were included. Of these, 7 studies evaluated the cost-effectiveness of regorafenib compared to trifluridine/tipiracil (TAS-102), 3 studies compared regorafenib with best supportive care (BSC), and 3 studies compared regorafenib with fruquintinib, serplulimab, and regorafenib dose optimization (ReDo).The quality of the included studies was high with an average QHES scores of 85.62. Regorafenib standard dose proves to be less cost-effective than alternative third-line therapies. Implementing a dose optimization strategy could potentially rectify this disparity and enhance the cost-effectiveness of regorafenib. Conclusion: The use of the standard dose of regorafenib is generally regarded as not cost-effective when compared to other third-line therapies for patients with mCRC. However, implementing a dose-escalation strategy may enhance regorafenib's cost-effectiveness. Consequently, significant price reductions or optimizing the dose of regorafenib are required to achieve cost-effectiveness.

Investigation of metal concentration distribution and corresponding health exposure assessment of fabricated metal product manufacturers.

The fabricated metal product industries were identified as producers of variable and heterogeneous pollution. Workers in these manufacturing facilities are exposed to multiple pollutants present at variable concentrations. Specific known adverse health effects include bladder cancer associated with metalworking fluid exposure and lung cancer associated with electroplating processes. To reduce the incidence of these adverse effects, the main challenge is to identify the most hazardous pollutants within this complex exposure environment and evaluate the corresponding health potentials. In this study, exposure indices were formulated to assess multiple metal exposures with the ultimate goal of providing relevant information for exposure reduction and control measures. Fifteen plants, including metal mold manufacturing, metal casting, and surface treatment plants, were investigated in terms of total concentration, summation of corresponding ratio to threshold limit value (STLVr), hazard index (HI), and incremental cancer risk. The results revealed that emissions of aluminum, iron, and manganese were primarily found in the metal mold manufacturing/casting plants, while emissions of chromium, nickel, and zinc were found in surface treatment plants. STLVr and HI were more useful than the total concentration for identifying hazardous metals, which were chromium and nickel, and could specify the facilities that were in need of control measures. As for cancer risk, the metal mold manufacturing/casting plants had lower risk than the surface treatment plants, and the contributing metals for these two plant types were cobalt and chromium, respectively. This study established a useful procedure to evaluate health hazards and cancer risk. The resulting information is useful for prioritizing mitigation control of multiple metal exposures.

Incremental Healthcare Costs of Diabetes Mellitus in a Middle-Income Country Using Administrative Healthcare Data.

OBJECTIVES: To estimate the incremental medical cost of diabetes mellitus using information from administrative databases in Colombia. METHODS: We carried out a retrospective cohort study with administrative health databases from Colombian population affiliated in the contributory health insurance scheme. We used an operative definition to select the cohort with diabetes. Incremental cost and cost ratio of diabetes were estimated using an inverse probability weighting of treatment approach to find the causal effect of having the disease. Weights were calculated by a propensity score method using a Random Forest model. The flexibility of this machine learning algorithm allows to have a better specification and bias reduction. Additionally, we reported incremental costs and cost ratios with confidence intervals using bootstrapping and analyzed costs by age groups and complications associated with diabetes. RESULTS: The estimated prevalence of diabetes was 2834 per 100 000 cases, in 2018. The group with diabetes was comprised 634 015 people and the control group 1 524 808. The calculated annual direct medical cost was $860, for which the incremental cost was $493 and the cost ratio 2.34. The incremental annual cost for some type of complication ranges from $1239 to $2043, renal complication being the most expensive. Incremental cost by age groups ranges from $347 to $878, being higher in younger people. CONCLUSIONS: Although the cost of diabetes in Colombia ranges among the global averages and is similar to other Latin-American countries, a greater incremental cost was found in patients with renal, circulatory, and neurologic complications.

Expandable-RCNN: toward high-efficiency incremental few-shot object detection.

This study aims at addressing the challenging incremental few-shot object detection (iFSOD) problem toward online adaptive detection. iFSOD targets to learn novel categories in a sequential manner, and eventually, the detection is performed on all learned categories. Moreover, only a few training samples are available for all sequential novel classes in these situations. In this study, we propose an efficient yet suitably simple framework, Expandable-RCNN, as a solution for the iFSOD problem, which allows online sequentially adding new classes with zero retraining of the base network. We achieve this by adapting the Faster R-CNN to the few-shot learning scenario with two elegant components to effectively address the overfitting and category bias. First, an IOU-aware weight imprinting strategy is proposed to directly determine the classifier weights for incremental novel classes and the background class, which is with zero training to avoid the notorious overfitting issue in few-shot learning. Second, since the above zero-retraining imprinting approach may lead to undesired category bias in the classifier, we develop a bias correction module for iFSOD, named the group soft-max layer (GSL), that efficiently calibrates the biased prediction of the imprinted classifier to organically improve classification performance for the few-shot classes, preventing catastrophic forgetting. Extensive experiments on MS-COCO show that our method can significantly outperform the state-of-the-art method ONCE by 5.9 points in commonly encountered few-shot classes.

Adaptive recognition of machining features in sheet metal parts based on a graph class-incremental learning strategy.

The integration of computer-aided design (CAD), computer-aided process planning (CAPP), and computer-aided manufacturing (CAM) systems is significantly enhanced by employing deep learning-based automatic feature recognition (AFR) methods. These methods outperform traditional, rule-based approaches, particularly in handling the complexities of intersecting features. However, existing deep learning-based AFR methods face two major challenges. The initial challenge stems from the frequent utilization of voxelized or point-cloud representations of CAD models, resulting in the unfortunate loss of valuable geometric and topological information inherent in original Boundary representation (B-Rep) models. The second challenge involves the limitation of supervised deep learning methods in identifying machining features that are not present in the predefined dataset. This constraint renders them suboptimal for the continually evolving datasets of real industrial scenarios. To address the first challenge, this study introduces a graph-structured language, Multidimensional Attributed Face-Edge Graph (maFEG), crafted to encapsulate the intricate geometric and topological details of CAD models. Furthermore, a graph neural network, Sheet-metalNet, is proposed for the efficient learning and interpretation of maFEGs. To tackle the second challenge, a three-component incremental learning strategy is proposed: an initial phase of pre-training and fine-tuning, a prototype sampling-based replay, and a stage employing knowledge distillation for parameter regularization. The effectiveness of Sheet-metalNet and its complementary incremental learning strategy is evaluated using the open-source MFCAD++ dataset and the newly created SMCAD dataset. Experimental results show that Sheet-metalNet surpasses state-of-the-art AFR methods in machining feature recognition accuracy. Moreover, Sheet-metalNet demonstrates adaptability to dynamic dataset changes, maintaining high performance when encountering newly introduced features, thanks to its innovative incremental learning strategy.

Exposure to workplace bullying: the incremental effect of gelotophobia beyond the big five.

The role of Big Five personality traits in exposure to workplace bullying has been a focus of numerous studies. Yet less is known about the incremental validity of narrower personality constructs. The aim of the present study was to investigate the incremental effect of gelotophobia (the fear of being laughed at) in predicting exposure to workplace bullying beyond the Big Five personality domains. The sample comprised 328 employees (77% females) from different regions of the Czech Republic. Correlational analysis showed that negative emotionality and gelotophobia were related to workplace bullying in theoretically expected ways. Results from a multiple regression indicated that gelotophobia had an incremental effect in predicting exposure to workplace bullying over and above the personality domains. Overall, this study provides new insights and extends previous investigations concerning the role of gelotophobia in workplace bullying. We also discuss the limitations of our study and provide suggestions for future research.

Cost-Effectiveness of Medical Therapy for Heart Failure With Mildly Reduced and Preserved Ejection Fraction.

BACKGROUND: Three medications are now guideline-recommended treatments for heart failure with mildly reduced or preserved ejection fraction (HFmrEF/HFpEF), however, the cost-effectiveness of these agents in combination has yet to be established. OBJECTIVES: The purpose of this study was to determine the cost-effectiveness of mineralocorticoid receptor antagonists (MRA), angiotensin receptor-neprilysin inhibitors (ARNIs), and sodium glucose co-transporter 2 inhibitors (SGLT2is) in individuals with HFmrEF/HFpEF. METHODS: Using a 3-state Markov model, we performed a cost-effectiveness study using simulated cohorts of 1,000 patients with HFmrEF and HFpEF. Treatment with 1-, 2-, and 3-drug combinations was modeled. Based on a United States health care sector perspective, outcome data was used to calculate incremental cost-effectiveness ratios (ICERs) in 2023 United States dollars based on a 30-year time horizon. RESULTS: Treatment with MRA, MRA+SGLT2i, and MRA+SGLT2i+ARNI therapy resulted in an increase in life years of 1.04, 1.58, and 1.80 in the HFmrEF subgroup, respectively, and 0.99, 1.54, and 1.77 in the HFpEF subgroup, respectively, compared with placebo. At a yearly cost of $18, MRA therapy resulted in ICERs of $10,000 per quality-adjusted life year (QALY) in both subgroups. The ICER for the addition of SGLT2i therapy ($4,962 per year) was $113,000 per QALY in the HFmrEF subgroup and $141,000 in the HFpEF subgroup. The addition of ARNI therapy ($5,504 per year) resulted in ICERs >$250,000 per QALY in both subgroups. If SGLT2i and ARNI were available at generic pricing the ICERs become <$10,000 per QALY in both EF subgroups. Outcomes were highly sensitive to assumed benefit in cardiovascular death. CONCLUSIONS: For patients with heart failure, MRA was of high value, SGLT2i was of intermediate value, and ARNI was of low value in both HFmrEF and HFpEF subgroups. For patients with HFmrEF/HFpEF increased use of MRA and SGLT2i therapies should be encouraged and be accompanied with efforts to lower the cost of SGLT2i and ARNI therapies.