Respective Categories Research Articles

Design and Evaluation of English Vocabulary Learning Aids Based on Word Vector Modelling

English vocabulary learning aids based on word vector modeling involve creating tools that leverage advanced techniques to enhance vocabulary acquisition. Word vector modeling, often using methods like Word2Vec or GloVe, represents words as high-dimensional vectors capturing semantic relationships. These models can power vocabulary learning aids by offering context-based word suggestions, personalized word quizzes, or interactive visualizations of word associations.. The paper introduces the Hierarchical Spiking Vocabulary Deep Learning (HSV-DL) framework, a novel approach aimed at enhancing vocabulary learning and classification tasks. With word vector modeling and spiking neural networks, HSV-DL offers a sophisticated methodology for accurately categorizing vocabulary words into their respective semantic categories. The Hierarchical Spiking Vocabulary Deep Learning (HSV-DL) framework introduces novel methodologies for vocabulary learning and classification tasks, achieving outstanding performance metrics. Experimental results demonstrate high accuracy (95%), precision (96%), recall (94%), and F1-score (95%) in categorizing vocabulary words into their semantic categories. Moreover, HSV-DL exhibits robustness to noise and efficient resource utilization, showcasing its potential for real-world applications in natural language processing.

TQRFormer: Tubelet query recollection transformer for action detection

Spatial and temporal action detection aims to precisely locate actions while predicting their respective categories. The existing solution, TubeR (Zhao et al., 2022), is designed to directly detect action tubes in videos by recognizing and localizing actions using a unified representation. However, a potential challenge arises during the decoding stage, leading to a gradual decrease in the model's performance in action detection, specifically in terms of the confidence associated with detected actions. In this paper, we propose TQRFormer: Tubelet Query Recollection Transformer, enabling the subsequent decoder to obtain information from the previous stage. Specifically, we designed Query Recollection Attention to correct errors and output the synthesized results, effectively breaking the limitations of sequential decoding. During the training stage, TubeR (Zhao et al., 2022) generates a limited number of positive sample queries through a one-to-one matching strategy, potentially impacting the effectiveness of training with positive samples. To enhance the quantity of positive samples, we propose a stage matching approach that combines both one-to-many matching and one-to-one matching without additional queries. This approach serves to boost the overall number of positive samples for improved training outcomes. We also propose a more elegant classification head that contains the start and end frames of the small tubes information, eliminating the necessity for a separate action switch. The performance of TQRFormer is superior to previous state-of-the-art technologies on public action detection datasets, including AVA, UCF101–24, JHMDB-21 and MultiSports. The code will available at https://github.com/ykyk000/TQRFormer.

Exploring the role of body mass index-adjusted calf circumference within the SARC-CalF screening tool among older patients with cancer

ObjectivesThis study aimed to assess and compare the frequency of positive scores using unadjusted SARC-CalF with the scores derived from SARC-CalF after adjusting calf circumference (CC) for body mass index (BMI). The secondary aim was to assess the prognostic value of SARC-CalF after BMI adjustment, for length of hospital stay (LOS) and mortality. Design, Setting, and ParticipantsThis secondary analysis of a prospective cohort study, included both outpatients and inpatients of an oncology unit hospital in Brazil. MeasurementsBMI and CC were measured. Patients with excess weight had their CC adjusted for BMI by subtracting 3 cm, 7 cm, and 12 cm from the unadjusted CC values for respective BMI categories. SARC-CalF was used to screen for sarcopenia. Scores ≥11 were indicative of sarcopenia, considering both unadjusted and BMI-adjusted CC values. Clinical outcomes included prolonged LOS and both short- and long-term mortality. ResultsOur study included 206 subjects, with a median age of 69 years, and the majority were males (52.1%). The prevalence of low CC increased from 65% to 84% after BMI adjustment. Positive unadjusted SARC-CalF scores (≥11) were observed in 51% of the population and this prevalence increased to 65% using BMI-adjusted SARC-CalF criteria (≥11). Higher scores on BMI-adjusted SARC-CalF but not unadjusted SARC-CalF were independently associated with prolonged LOS [adjusted HR: 1.26 (1.03–1.53)], and 6-month mortality [adjusted HR: 1.42 (1.07–1.87)]. Both unadjusted and BMI-adjusted SARC-CalF were independently associated with 12-month mortality. ConclusionBMI-adjusted SARC-CalF may be a promising strategy to enhance the detection of older patients with cancer and excess weight at risk of sarcopenia, and it may serve a dual role as a prognostic tool, as it was independently associated with prolonged LOS and mortality.

Feature alignment via mutual mapping for few-shot fine-grained visual classification

Few-shot fine-grained visual classification aims to identify fine-grained concepts with very few samples, which is widely used in many fields, such as the classification of different species of birds in biological research, and the identification of car models in traffic monitoring. Compared with the common few-shot classification task, it encounters difficulties due to significant variations within each class and small gaps between different categories. To address such problems, previous studies primarily project support samples into the space of query samples and employ metric learning to classify query images into their respective categories. However, we observe that such methods are not effective in resolving inter-class variations. To overcome this limitation, we propose a new feature alignment method based on mutual mapping, which simultaneously considers the discriminative features of new samples and classes. Specifically, besides projecting support samples into the space of query samples for reducing intra-class variations, we also project query samples into the space of support samples to increase inter-class variations. Furthermore, a direct position self-reconstruction module is proposed to utilize the location information of objects and obtain more discriminative features. Extensive experiments on four fine-grained benchmarks demonstrate that our approach is competitive when compared with other state-of-the-art methods, in both 1-shot and 5-shot settings. In the case of 5-shot, our method achieved the best performance on all four datasets, with 92.11%, 85.31%, 96.09%, and 94.64% accuracies on CUB-200-2011, Stanford Dogs, Stanford Cars, and Aircaft, respectively.

Research on the Optimization of Pricing and the Replenishment Decision-Making Problem Based on LightGBM and Dynamic Programming

Vegetables have a short period of freshness, and therefore, the purchase of vegetables has to be carefully matched with sales, especially in the “small production and big market” setting prevalent in China. Therefore, it is worthwhile to develop a systematic and comprehensive mathematical model of replenishment plans and pricing strategies for each category of vegetables and individual products. In this paper, we analyze the following three questions: Question One: What is the distribution law and relationship between the sales volume of vegetable categories and single products? Question Two: What is the relationship between total sales volume and cost-plus pricing of vegetable categories? And is it possible to provide the daily total replenishment and pricing strategy of each vegetable category for the following week to maximize supermarket profit? Question Three: How can we incorporate the market demand for single vegetable products into a profit-maximizing program for supermarkets? Is it possible to further formulate the replenishment plan requirements for single products? To answer the first question, we created pivot tables to analyze occupancy. We found that mosaic leaves, peppers, and edible mushrooms accounted for a larger proportion of occupacy, while cauliflowers, aquatic rhizomes, and eggplants accounted for a smaller proportion. For the single items, lettuce, cabbage, green pepper, screw pepper, enoki mushroom, and shiitake mushroom accounted for a large proportion of their respective categories. We used the Pearson correlation coefficient and the Mfuzz package based on fuzzy c-means (FCM) algorithm to analyze the correlation between vegetable categories and single products. We found that there was a strong correlation between vegetable categories. Moreover, the sale of vegetable items belonging to the same category exhibited the same patterns of change over time. In order to address the second question, we established the LightGBM sales forecasting model. Combined with previous sales data, we forecasted and planned an efficient daily replenishment volume for each vegetable category in the coming week. In addition, we developed a pricing strategy for vegetable categories to maximize supermarket profits. For the third question, we built a dynamic programming model combining an optimal replenishment volume with a product pricing strategy for single items, which let the supermarket maximize its expected profits.

Outcomes of endovascular thrombectomy for ischaemic stroke among nonagenarians with and without pre-existing dependency

BackgroundOur aim was to compare the outcomes of treatment with endovascular thrombectomy for pre-stroke dependent versus pre-stroke independent among the very elderly ≥90 years. MethodsA retrospective cohort study was performed on 106 patients ≥90 years who underwent endovascular thrombectomy for ischaemic stroke in Western Australia between June 2016 and September 2021. Patient, stroke and procedural details along with inpatient progress and outcome at 90 days were recorded. Patients were divided into Group A (pre-stroke modified Rankin Scale 0-2) and Group B (pre-stroke modified Rankin Scale >2). Primary outcome measure was functional status at 90 days post-stroke, with favourable clinical outcome defined as a 90-day mRS category equal to the patients’ respective pre-stroke mRS category. Secondary outcome measures include successful reperfusion, symptomatic intracranial haemorrhage, hospital length-of-stay, change in accommodation to an aged care facility, and mortality during admission, at 90 days and one year. Results61 patients were allocated to Group A and 45 to Group B. There was none with pre-stroke mRS 5. Group B had more pre-existing cognitive impairment, aged care facility residents, higher median age and lower Alberta Stroke Program Early CT Score. For primary outcome measure, 38 % of patients in Group A and 49 % in Group B achieved a favourable clinical outcome. The difference was not significant (p=0.3408). For secondary outcome measures, Group B had a significantly higher 90-day mortality rate at 47 % versus 24 % in Group A (p=0.03). All other secondary outcome measures were similar between the two groups. These include the rate of successful reperfusion and symptomatic intracranial haemorrhage, hospital length-of-stay, new transition into an aged care facility, inpatient mortality rate and 1-year mortality rate. ConclusionWhen treated with endovascular thrombectomy for ischaemic stroke, nonagenarians with pre-existing dependency achieved a rate of favourable functional outcome comparable with their independent peers, although they also had higher 90-day mortality rate.

Design of an Automatic Classification System for Educational Reform Documents Based on Naive Bayes Algorithm

With the continuous deepening of educational reform, a large number of educational policies, programs, and research reports have emerged, bringing a heavy burden of information processing and management to educators. Traditional manual classification and archiving methods are inefficient and susceptible to subjective factors. Therefore, an automated method is needed to quickly and accurately classify and archive documents into their respective categories. Based on this, this paper proposes a design of an automatic document classification system for educational reform based on the Naive Bayes algorithm to address the challenges of document management in the education field. Firstly, the relevant literature and document data in the field of educational reform are collected and organized to establish an annotated dataset for model detection. Secondly, the raw data are preprocessed by cleaning and transforming the original text data to make them more suitable for input into machine learning algorithms. Thirdly, various algorithms are trained and selected to determine the best algorithm for classifying educational reform documents. Finally, based on the determined algorithm, a corresponding classification software is designed to automatically classify and archive educational reform documents for analysis. Through experimental evaluation and result analysis, this research demonstrates the effectiveness and accuracy of the education reform document automatic classification system based on the Naive Bayes algorithm. This method can efficiently classify a large number of documents into their respective categories quickly and accurately, thereby improving the efficiency of educators and their information management capabilities. In the future, further exploration of feature extraction methods and machine learning algorithms can be conducted to optimize the classification performance and apply this method to practical management and decision-making in the education field.

Analysis of Oklahoma amputation trends and identification of risk factors to target areas for limb preservation interventions

ObjectiveAnnual trends of lower extremity amputation due to end-stage chronic disease are on the rise in the United States. These amputations are leading to massive expenses for patients and the medical system. In Oklahoma, we have a high-risk population because access to care is low, the number of uninsured is high, cardiovascular health is poor, and our overall health care performance is ranked 50th in the country. But we know little about Oklahomans and their risk of limb loss. It is, therefore, imperative to look closely at this population to discover contemporary rates, trends, and state-specific risk factors for amputation due to diabetes and/or peripheral arterial disease (PAD). We hypothesize that state-specific groups will be identified as having the highest risk for limb loss and that contemporary trends in amputations are rising. To create implementable solutions to limb preservation, a baseline must be set. MethodsWe conducted a 12-consecutive-year observational study using Oklahoma’s hospital discharge data. Discharges among patients 20 years or older with a primary or secondary diagnosis of diabetes and/or PAD were included. Diagnoses and amputation procedures were identified using International Classification of Disease-9 and -10 codes. Amputation rates were calculated per 1000 discharges. Trends in amputation rates were measured by annual percentage changes (APC). Prevalence ratios evaluated the differences in amputation rates across demographic groups. ResultsOver 5,000,000 discharges were identified from 2008 to 2019. Twenty-four percent had a diagnosis of diabetes and/or PAD. The overall amputation rate was 12 per 1000 discharges for those with diabetes and/or PAD. Diabetes and/or PAD-related amputation rates increased from 8.1 to 16.2 (APC, 6.0; 95% confidence interval [CI], 4.7-7.3). Most amputations were minor (59.5%), and although minor, increased at a faster rate compared with major amputations (minor amputation APC, 8.1; 95% CI, 6.7-9.6 vs major amputation APC, 3.1; 95% CI, 1.5-4.7); major amputations were notable in that they were significantly increasing. Amputation rates were the highest among males (16.7), American Indians (19.2), uninsured (21.2), non-married patients (12.7), and patients between 45 and 49 years of age (18.8), and calculated prevalence ratios for each were significant (P = .001) when compared within their respective category. ConclusionsAmputation rates in Oklahoma have nearly doubled in 12 years, with both major and minor amputations significantly increasing. This study describes a worsening trend, underscoring that amputations due to chronic disease is an urgent statewide health care problem. We also present imperative examples of amputation health care disparities. By defining these state-specific areas and populations at risk, we have identified areas to pursue and improve care. These distinctive risk factors will help to frame a statewide limb preservation intervention.

INOVASI PEMANFAATAN TEKNOLOGI INFORMASI DALAM MENINGKATKAN EFISIENSI MANAJEMEN PENDIDIKAN DI MIS 05 DARUSSALAM

The utilization of information technology innovation has become a crucial focus in efforts to enhance the efficiency of educational management at MIS 05 Darussalam. The fundamental concept of educational management, particularly educational planning, serves as the cornerstone in achieving optimal educational objectives. This research aims to comprehensively analyze the utilization of information technology innovation in improving educational management efficiency and to identify challenges and obstacles encountered during the implementation of information technology at MIS 05 Darussalam. This research employs a qualitative and descriptive research approach. The data analysis technique categorizes the gathered data based on their respective categories, which will be analyzed through three qualitative data analysis steps, including data reduction, presentation, and conclusion drawing. The results of this research reveal tangible efforts to integrate information technology, especially in administrative aspects such as student data management, schedule arrangement, and attendance recording. However, several challenges, such as budget constraints, insufficient training, and technology accessibility, hinder its implementation. These challenges manifest in the form of classrooms that have not fully utilized technological facilities, encompassing both hardware maintenance and educators' understanding of effectively integrating technology. Overcoming these challenges requires strategic measures, including prudent budget allocation, continuous training, and policy development that supports effective integration. Positive implications include improved management efficiency, transparency, parental involvement, and enriched learning through E-learning.

Overcoming denominator problems in refugee settings with fragmented electronic records for health and immigration data: a prediction-based approach.

Epidemiological studies in refugee settings are often challenged by the denominator problem, i.e. lack of population at risk data. We develop an empirical approach to address this problem by assessing relationships between occupancy data in refugee centres, number of refugee patients in walk-in clinics, and diseases of the digestive system. Individual-level patient data from a primary care surveillance system (PriCarenet) was matched with occupancy data retrieved from immigration authorities. The three relationships were analysed using regression models, considering age, sex, and type of centre. Then predictions for the respective data category not available in each of the relationships were made. Twenty-one German on-site health care facilities in state-level registration and reception centres participated in the study, covering the time period from November 2017 to July 2021. 445 observations ("centre-months") for patient data from electronic health records (EHR, 230 mean walk-in clinics visiting refugee patients per month and centre; standard deviation sd: 202) of a total of 47.617 refugee patients were available, 215 for occupancy data (OCC, mean occupancy of 348 residents, sd: 287), 147 for both (matched), leaving 270 observations without occupancy (EHR-unmatched) and 40 without patient data (OCC-unmatched). The incidence of diseases of the digestive system, using patients as denominators in the different sub-data sets were 9.2% (sd: 5.9) in EHR, 8.8% (sd: 5.1) when matched, 9.6% (sd: 6.4) in EHR- and 12% (sd 2.9) in OCC-unmatched. Using the available or predicted occupancy as denominator yielded average incidence estimates (per centre and month) of 4.7% (sd: 3.2) in matched data, 4.8% (sd: 3.3) in EHR- and 7.4% (sd: 2.7) in OCC-unmatched. By modelling the ratio between patient and occupancy numbers in refugee centres depending on sex and age, as well as on the total number of patients or occupancy, the denominator problem in health monitoring systems could be mitigated. The approach helped to estimate the missing component of the denominator, and to compare disease frequency across time and refugee centres more accurately using an empirically grounded prediction of disease frequency based on demographic and centre typology. This avoided over-estimation of disease frequency as opposed to the use of patients as denominators.

Going beyond summed stress scores: Correlating global and territorial coronary flow reserve by single photon emission tomography with routine myocardial perfusion imaging

(a) BackgroundTechnological advancement in the recent years has enabled the application of single photon emission tomography (SPECT) to evaluate myocardial blood flow (MBF). This method offers increased sensitivity in the assessment of coronary health, quantifiable through non-invasive imaging beyond the more conventional methods such as with myocardial perfusion imaging (MPI). (b) AimsTo correlate MBF, derived by dynamic SPECT, both global and by coronary territories to the summed stress scores (SSS) on conventional MPI. (c) MethodsImages obtained from dipyridamole-gated SPECT MPI stress and rest studies performed on recruited subjects were examined. We calculated the global and regional coronary flow reserve (CFR) via a standard software package, taken as the ratio of stress MBF to rest MBF, using CFR<2.5 as the cut off. (d) ResultsAmongst the 90 recruited subjects (mean age 67 ± 8 years; of which 76% were males), 49% had MPI within normal limits (summed stress score (SSS) 0–3; Left ventricular ejection fraction (LVEF) > 50%). We observed a progressive reduction in global and regional CFR across the normal SSS category to that of severely abnormal (SSS >13). Reduced global CFR with correspondent lower CFR across the regional arteries were detected in scans within normal limits of MPI scans in subjects who were older (69 ± 7 vs. 62 ± 9 years, p = 0.034). Decreasing CFR was significantly associated with increasing age across the regional arteries. (e) ConclusionIn our study we depict the global and regional MBF values obtained via SPECT MPI in correlation to the respective SSS categories. Our data proposes that dynamic SPECT has a part in refining cardiac risk stratification, particularly in the older adult population, who are at greater risk.

Strategic Design Approaches for Eliciting the Perception of ‘Prestige’ in Housing Consumers

In the current real estate landscape, there is a growing trend to focus on adding value to products to address the customer’s affective/emotional perspective, his/her perceptions of the ‘Prestige’ of properties being crucial for his/her final assessments of the products. This study delves into the design elements that shape perceptions of ‘Prestige’ using Kansei engineering in 235 participants who evaluated various real estate promotions through: (1) a set of 60 adjectives and (2) reason for purchase (residence or investment). A first factor analysis of the set of adjectives yielded 15 independent axes. A subsequent linear regression indicated that the ‘Prestige’ axis was among the four most important factors in/drivers of property purchase decisions. In the second phase, 62 design elements (and their respective categories) of real estate promotions were identified to establish 16 groups. A linear regression determined that Information presentation and Building were the groups of elements with the greatest impact on subjects’ perceptions of ‘Prestige’. A subsequent Univariate General Linear Model analysis identified the design elements significant for each group, such as Development type and Building shape, respectively. Finally, a Bonferroni post hoc test identified the important categories in each identified design element as Facing blocks and Stepped.

Seroprevalence of bovine viral diarrhea virus and detection of persistently infected (PI) animals in dairy farms of Holeta, central Ethiopia

Bovine viral diarrhea virus (BVDV) is one of the most important pathogens of the reproductive systems that have a significant socio economic impact on the dairy industry. A cross-sectional study was conducted on 17 randomly selected farms out of 133 registered dairy farms in Holeta, a town in the Walmera district of Ethiopia. The study aimed to detect BVDV antigen-positive animals and investigate the seroprevalence of BVDV infection. A total of 337 serum samples were collected from selected farms with no history of BVDV vaccination. The indirect enzyme-linked immunosorbent assay (I-ELISA) test was the screening test used to detect antibodies against BVDV. In contrast, antigencapture ELISA was used for the detection of BVDV antigens in seronegative animals. A logistic regression model was used to assess the association between potential predictors and BVDV exposure. The overall animal level seroprevalence was 15.4%, and 64.7% of the herds had at least one seropositive animal. From 285 seronegative animals, one animal (0.4%) was found positive for BVDV antigen. The same animal was found positive in a double check 21 days later. In this study, cows with a history of abortion (OR = 6.3; 95% CI: 1.61 -13.1), history of repeated breeding (OR = 7; 95% CI: 2.5 - 14.3), animals managed intensively (OR = 4.6; 95% CI: 1.6 - 13.0) and multiparous cows (OR = 3.6; 95% CI: 1.5 - 8.9) had higher proportion of seroreactors in their respective comparison category (p&lt;0.05). Besides, cows with a history of congenital defective calve birth (OR = 15.2; 95% CI: 3.2 - 73.6), adult age groups (OR = 2.9; 95% CI: 1.0-7.9), and cows bred both artificially and natural mating (OR = 4.6; 95% CI: 1.7 - 12.6) were statistically associated with BVDV seropositivity (p&lt;0.05). In conclusion, this report demonstrated the presence of persistently infected (PI) dairy cattle in Ethiopia. Thus, the findings warrant the need for immediate control intervention that involves both screening and culling of PI animals and vaccination.

Estimation Parameters in the fuzzy class Poisson mixture regression model

In the Poisson mixture regression for the fuzzy class model, observations originate from distinct sub-sources or classes due to the problem of heteroscedasticity. The underlying assumption posits that the observed data stem from a finite mixture of fuzzy classes. The primary challenge lies in achieving the optimal assignment of observations to their respective categories, necessitating the utilization of sophisticated methods for parameter estimation within the model. This research paper explores the estimation of parameters in a Poisson mixture regression model designed for the fuzzy class. Leveraging the FCML (Fuzzy Classification Maximum Likelihood algorithm) and genetic algorithm methodologies, we conducted simulations to assess the accuracy and efficacy of parameter estimation. Our findings reveal a clear superiority of the genetic algorithm over the FCML algorithm, as evidenced by the Mean Square Error (MSE) criterion. The genetic algorithm consistently produced lower MSE values, indicating more precise and reliable parameter estimates compared to the FCML algorithm. This research underscores the potential of the genetic algorithm as a robust and effective tool for parameter estimation in complex statistical models, offering researchers an alternative approach for tackling challenges in the estimation of parameters within the context of the fuzzy class

Audio-Visual Event Localization using Multi-task Hybrid Attention Networks for Smart Healthcare Systems

Human perception heavily relies on two primary senses: vision and hearing, which are closely inter-connected and capable of complementing each other. Consequently, various multimodal learning tasks have emerged, with audio-visual event localization (AVEL) being a prominent example. AVEL is a popular task within the realm of multimodal learning, with the primary objective of identifying the presence of events within each video segment and predicting their respective categories. This task holds significant utility in domains such as healthcare monitoring and surveillance, among others. Generally speaking, audio-visual co-learning offers a more comprehensive information landscape compared to single-modal learning, as it allows for a more holistic perception of ambient information, aligning with real-world applications. Nevertheless, the inherent heterogeneity of audio and visual data can introduce challenges related to event semantics inconsistency, potentially leading to incorrect predictions. To track these challenges, we propose a multi-task hybrid attention network (MHAN) to acquire high-quality representation for multimodal data. Specifically, our network incorporates hybrid attention of uni- and parallel cross-modal (HAUC) modules, which consists of a uni-modal attention block and a parallel cross-modal attention block, leveraging multimodal complementary and hidden information for better representation. Furthermore, we advocate for the use of a uni-modal visual task as auxiliary supervision to enhance the performance of multimodal tasks employing a multi-task learning strategy. Our proposed model has been proven to outperform the state-of-the-art results based on extensive experiments conducted on the AVE dataset.

The Economic Impact of Obesity in Turkey: A Micro-Costing Analysis.

Turkey currently has the highest obesity prevalence among its European counterparts. 32% and 61% of the population live with obesity and overweight, respectively. Overweight and obesity are linked to non-communicable diseases that incur incremental health and economic costs. The significant public health concern warrants an assessment of the cost of obesity. A micro-costing approach from the public payer perspective was conducted to estimate direct healthcare costs associated with ten obesity-related comorbidities (ORCs) in Turkey. Clinical practice guidelines and a systematic literature review informed ORCs and the respective cost categories. This was subsequently validated by a steering committee comprising seven experts. Seventy public sector physicians were surveyed to estimate healthcare resource use. Unit costs were derived from Social Security Institute's Healthcare Implementation Communique. Cost items were summed to determine the annual cost per patient per ORC, which was validated by the steering committee. Medical inflation was considered in a scenario analysis that varied resource unit costs. Chronic kidney disease, heart failure and type 2 diabetes are the costliest ORCs, incurring an annual cost of 28,600 TRY, 16,639 TRY and 11,993 TRY, respectively. Individuals in Turkey with any ORC triggered direct healthcare costs ranging 1857-28,600 TRY annually. Costs were driven by tertiary care resources arising from treatment-related adverse events, disease complications and inpatient procedures. In the scenario analysis, medical resource unit costs were inflated by 18.7% and 39.4%, triggering an average increase in cost across all ORCs of 1998 TRY and 4210 TRY, respectively. Our findings confirm that obesity and its complications result in significant financial burden to the public healthcare system. By quantifying the burden of obesity across a comprehensive spectrum of ORCs, our study aims to support the economic case for investing in appropriate obesity interventions.

Private Equity Investment in Surgical Care.

To characterize the extent of private equity investment affecting surgical care. Over the last decade, investor-backed, for-profit private equity groups have invested in healthcare at an unprecedented rate, but the breadth of these investments affecting surgical practice remains largely unknown. Four nationally representative databases were used to identify all merger/acquisitions involving surgical practices between 2015-2019, determine private equity investment in those transactions, and link the acquisitions with a physician dataset. 1,542 unique transactions were identified, of which 539 were financed by private equity. 58 transactions were then classified into their respective categories within surgical care: digestive disease, orthopedics, urology, vascular surgery, and plastic/cosmetic surgery. These transactions accounted for 199 practice sites and 1,405 physicians, averaging 24.2 physicians per transaction. Acquisition activity peaked in 2017 with a total of 63 practices involved. Digestive disease, urology, and orthopedic surgery accounted for the most activity. General surgeons were involved in a small share of the digestive disease practice acquisitions. Three "surgery-adjacent" categories were also identified: anesthesiology, ambulatory surgery centers, and surgical staffing firms. Among these, anesthesia was the largest category in terms of practices (194) and physicians (2,660) involved in transactions across the study period. Medical Service Organizations (MSOs) were a key mechanism through which private equity firms invested in surgical care. Private equity has engaged in substantial investment within surgical specialties, creating increased practice consolidation. These investments affect all levels of medical care and have notable implications for patients, practitioners, and policymakers.

Internal dynamics of patent reference networks using the Bray–Curtis dissimilarity measure

BackgroundPatents are indicators of technological developments. The science & technology categories, to which they are assigned to, form a directed, weighted network where the links are the references between patents belonging to the respective categories. This network can be conceived as a kind of intellectual ecology, lending itself to mathematical analyses analogous to those carried out in numerical ecology. The non-metric Bray–Curtis dissimilarity, commonly used in quantitative ecology, can be used to describe the internal dynamics of this network.ResultsWhile the degree-distribution of the network remained stable during the studied years, that of the sub-networks of with at least k links showed that k = 5 is a critical number of citations: this many are needed that the bias towards already highly cited works come into effect (preferential attachment). Using the dij Bay-Curtis dissimilarity between nodes i and j, a surprising pattern emerged: the log-probability of a change in dij during a quarter of year depended linearly, with a negative coefficient, on the magnitude of the change itself.ConclusionsThe developed methodology could be useful to detect emerging technological developments, to aid decisions, for example, on resource allocation. The pattern found on the internal dynamics of the system depends on the categorisation of the patents, therefore it can serve as an indicator when comparing different categorisation methods.Graphical

Urology never events in the United Kingdom: A retrospective 10‐year review

AbstractObjectivesThe aim was to assess the prevalence of never events (NEs) specific to urology in the United Kingdom and identify commonly occurring themes.MethodsData from the National Health Service (NHS) NEs website were obtained and all NEs from 2012 to 2022 were reviewed. Urology‐specific NEs were identified and further analysed in their respective categories. Data regarding the total number of surgical procedures performed in the NHS specific to each specialty were obtained via the NHS Hospital Episode Statistics website.ResultsThere were 3972 NEs recorded over the 10‐year period with 95 (2.4%) of these as a result of urology surgery. The most common surgical intervention associated with a urological NE was ureteric stenting, which comprised 45/95 (47.4%) of all analysed NEs. These consisted of wrong site ureteric stent insertion (n = 29), wrong site ureteric stent removal (n = 9), wrong stent type (n = 5) and retained guidewires (n = 2). There were 7.14 million urology surgeries performed in the 10‐year period, and prevalence was 0.0013%.ConclusionNEs are fully preventable serious incidents in the NHS. This is the first study to investigate the prevalence of NEs in urology in the United Kingdom. This study demonstrates that in the last 10 years the prevalence of urology NEs is low at 0.0013%, with ureteric stent procedures accounting for more than half of the NEs. Urologists should be mindful of the potential for wrong site surgery in urologic stenting procedures.

Abstract A038: Evaluating DCIS progression: A comparative analysis of CNA predictive power derived from lpWGS and WES data

Abstract Ductal carcinoma in situ (DCIS) is a very common non-life threatening, pre-invasive form of breast cancer constituting 25% of all new breast cancer diagnoses in the USA, and is normally treated with invasive measures. However, only 20 to 30% of DCIS cases will progress to life-threatening invasive breast cancer in their lifetime. There is a need for patient stratification to decrease treatment burden and focus resources on patients who actually require treatment. To ameliorate this issue, we aim to develop evolutionary biomarkers that predict both DCIS recurrence and its progression to invasive breast cancer. We conducted an observational longitudinal study in which two DCIS samples from each primary tumor were acquired prior to treatment—to account for intratumor heterogeneity—and followed up with (median event-free patient observation time: 106 months) to classify each patient’s outcome into its respective cohort category of [1] no DCIS recurrence, [2] DCIS recurrence, or [3] invasive ductal carcinoma (IDC) recurrence (progression). Each of the three cohort groups comprises approximately 30 patients, for which our primary data includes low-pass whole genome sequencing (lpWGS) and whole exome sequencing (WES) of the two primary DCIS samples, in addition to WES data for normal tissue control, clinical and epidemiological covariates, and time to recurrence. Preliminary estimates of copy number alterations (CNAs) generated using lpWGS data show that CNA burden, representing the proportion of altered genome, has statistically significant predictive capability for DCIS progression (Univariate Cox regression: p=0.014, HR=1.5). CNA divergence, representing the proportion of non-overlapping altered genome, has predictive capabilities of both recurrence and progression (Univariate Cox regression: Recurrence: p=0.021, HR=0.74; Progression: p=0.024, HR=0.67). In this work, our primary objective is to test if CNAs generated utilizing WES data are comparable in their predictive power to those yielded by lpWGS data for the same human DNA samples. It is not expected that the copy number alteration (CNA) estimates will be identical between the two methods, but rather that mutational burden and intratumor heterogeneity estimated using different data sources are strongly correlated and will have similar predictive capabilities. If we successfully confirm that CNAs estimated with both lpWGS and WES data have similar predictive power of DCIS progression, we will then be able to validate our evolutionary biomarkers using a different cohort for which we only possess WES data. This would also expand the applicability of our evolutionary biomarkers to a larger number of pre-existing datasets. We ultimately aim to develop strong evolutionary biomarkers that, when utilized alongside evolutionary therapies, will revolutionize the clinical management of cancer with the potential to improve both patient prognoses and quality-of-life outcomes. Citation Format: Manasa Iyer, Diego Mallo, Carlo C. Maley, Angelo Fortunato, Luis Cisneros, Lorraine M. King, Marc D. Ryser, Joseph Y. Lo, Allison Hall, Jeffrey R. Marks, Shelley Hwang. Evaluating DCIS progression: A comparative analysis of CNA predictive power derived from lpWGS and WES data [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A038.