Daily Volume Research Articles

In-hospital bioimpedance-derived total body water predicts short-term cardiovascular mortality and re-hospitalizations in acute decompensated heart failure patients.

Hospital re-admissions in heart failure (HF) patients are mostly caused by an acute exacerbation of their chronic congestion. Bioimpedance analysis (BIA) has emerged as a promising non-invasive method to assess the volume status in HF. However, its correlation with clinically assessed volume status and its prognostic value in the acute intra-hospital setting remains uncertain. In this single-center observational study, patients (n = 49) admitted to the cardiology ward for acute decompensated HF (ADHF) underwent a daily BIA-derived volume status assessment. Median hospital stay was 7 (4-10) days. Twenty patients (40%) reached the composite endpoint of cardiovascular mortality or re-hospitalization for HF over 6months. Patients at discharge displayed improved NYHA class, lower body weight, plasma and blood volume, as well as lower NT-proBNP levels compared to the admission. Compared to patients with total body water (TBW) less than or equal to that predicted by body weight, those with higher relative TBW levels had elevated NT-proBNP and E/e´ (both p < 0.05) at discharge. In the Cox multivariate regression analysis, the BIA-derived delta TBW between admission and discharge showed a 23% risk reduction for each unit increase (HR = 0.776; CI 0.67-0.89; p = 0.0006). In line with this finding, TBW at admission had the highest prediction importance of the combined endpoint for a subgroup of high-risk HF patients (n = 35) in a neural network analysis. In ADHF patients, BIA-derived TBW is associated with the increased risk of HF hospitalization or cardiovascular death over 6months. The role of BIA for prognostic stratification merits further investigation.

Diuresis-led volume replacement strategy in extremely low birth weight infants.

In extremely low birth weight infants, fluid overload has been associated with bronchopulmonary dysplasia and death. Excessive weight loss may increase the risk of meconium obstruction and intestinal perforation. As these infants display oliguria followed by polyuria, we embarked on a diuresis-led volume replacement strategy as of January 2020. This single-centre analysis presents data of infants <1000 g birth weight surviving for more than 3 days admitted 2017-2019 (n = 217, daily volume increase) versus 2020-2022 (n = 2022, diuresis-led volume replacement). The second cohort had lower gestational age (median [interquartile range]: 253/7 [243/7-264/7] vs. 263/7 [251/7-282/7] weeks), less antenatal steroids (58% vs. 69%), more indomethacin (66% vs. 47%) and higher initial diuresis (5.6 [4.9-6.8] vs. 4.8 [4.2-5.5] mL/kg/h) but did not differ by relative weight loss at Day 7 of life. Employing binary logistic regression with gestational age, antenatal steroids and indomethacin as covariates, the cohorts did not differ by rates of patent ductus arteriosus, abdominal surgery or severe retinopathy of prematurity, while there were significant declines in sepsis (from 32% to 19%), bronchopulmonary dysplasia (from 26% to 23%) and mortality (from 13% to 7%). Diuresis-led volume replacement appears to be safe and maybe beneficial.

Forecasting Daily Radiotherapy Patient Volumes in a Tertiary Hospital Using Autoregressive Integrated Moving Average (ARIMA) Models.

The purpose is to predict the volume of patients treated daily with radiotherapy using the autoregressive integrated moving average (ARIMA) model. In this retrospective study, data from the billing records detailing daily radiotherapy treatment sessions were extracted from the Hospital Information System and analyzed. The study included all patients treated from January 2004 to December 2022. The analysis was divided into two parts: First, the data were summarized using descriptive statistics. Second, time series forecasting with the implementation of an ARIMA model for estimating patient volumes. For the ARIMA modeling process, the Akaike Information Criterion (AIC) was used for classical model optimization. The Mean Absolute Percentage Error (MAPE) was used for evaluating between different models. Residual analysis was performed in each model using the Ljung-Box test, Jarque-Bera test, and heteroskedasticity test to identify autocorrelation, normal distribution, and variances that could undermine the reliability of the model. A total of 895,808 radiotherapy sessions were included in the study. The median number of radiotherapy sessions per day was 181 (150, 205). A clear transition to more modern radiotherapy equipment, particularly the Truebeam accelerator, was observed, indicating a growing dependency on advanced techniques such as volumetric-modulated arc therapy (VMAT), stereotactic body radiation therapy (SBRT), and stereotactic radiosurgery (SRS). The best ARIMA model predicted an increase in demand, projecting an average daily patient volume of 279.40 by 2030. The study highlights the need for advanced forecasting methodologies in healthcare resource planning and emphasizes the importance of considering environmental and external factors for effective and accurate resource allocation strategies.

Reinforcement Learning Algorithm for Optimising Durian Irrigation Systems: Maximising Growth and Water Efficiency

This study presents a Reinforcement Learning-based algorithm designed to optimise irrigation for Durio Zibethinus (i.e., durian) trees, aiming to maximise tree growth and reduce water usage. Traditional irrigation methods, as well as current machine learning models, often focus only on soil moisture and weather data, neglecting critical factors like actual tree growth. This study proposed a reinforcement learning irrigation (RL-Irr) algorithm incorporating tree growth stages, soil moisture, and weather conditions to determine precise irrigation needs. The algorithm was developed by calibrating the AQUACROP model using data from actual durian plantations where rain-fed irrigation (rain-fed) was practised. Daily irrigation volumes were calculated based on real-time soil moisture, weather forecasts, and weekly tree growth measurements. The reinforcement learning method was used to optimise irrigation schedules, with rewards based on soil moisture, tree growth, rainfall, and weather conditions. The algorithm was tested using AQUACROP simulations and compared against soil moisture balance irrigation (SMB-Irr) and rain-fed. The results showed that the RL-Irr reduced water use by up to 75 percent while maintaining tree growth. These findings suggest the algorithm could significantly improve water efficiency in durian farming, though real-world applications should consider potential model limitations.

Research on the Application of Statistical Anomaly Detection in Tobacco Sales Anomaly Detection

With the rapid development of a new generation of information technology, digitalization and informatization have become key factors in enhancing the core competitiveness of the tobacco industry. Detecting and monitoring abnormal tobacco sales behaviors play a vital role in combating illegal transactions and ensuring the healthy development of the tobacco industry. Manual identification of abnormal sales behaviors presents practical issues, such as low efficiency, strong subjectivity, and a reliance on accumulated experience. Utilizing statistical probability theory to help the tobacco bureau determine the threshold for defining abnormal sales behavior can effectively improve the efficiency of anomaly detection. This study applies relevant theories of statistical anomaly detection in combination with practical production needs, establishing four threshold criteria for identifying abnormal sales behaviors: the number of days without retail data, the ratio of daily sales volume to the sales volume of the previous seven days, the number of days of available sales, and the number of days with scanning activity per week. These criteria make it possible to automate and dynamically identify abnormal sales behaviors in subsequent sales situations.

The cost of COVID-19 vaccine delivery in Bangladesh

ABSTRACT COVID-19 vaccination has been instrumental in fighting the pandemic, but evidence on the actual costs associated with delivering these vaccines in resource-constrained settings has been limited. We estimated the cost of delivering COVID-19 vaccines in Bangladesh through five delivery strategies in 2021 and 2022, including Ministry of Health (MOH) hospitals, non-MOH government hospitals, outreach at Expanded Program on Immunization (EPI) centers, mass campaigns, and schools. This was a bottom-up costing study, estimating costs from a payer and beneficiary perspective. We also mapped the funding flows for COVID-19 vaccination activities and analyzed programmatic and financial challenges. The economic cost incurred by the health system to deliver COVID-19 vaccines was $1.05 per dose, excluding vaccine costs. This was made up of a financial cost of $0.29 per dose and an opportunity cost of $0.75 per dose. School-based delivery incurred the lowest financial cost of $0.27, while outreach at EPI centers incurred the highest at $0.44 per dose. The low financial cost per dose is attributed to the high daily volumes delivered at sampled sites, minimal additional resources provided to sites to implement the COVID-19 vaccination program, and a reliance on the existing workforce. Beneficiaries spent an average of $1.63 to receive a single dose of COVID-19 vaccination at fixed sites, with transport representing the largest cost driver ($0.75 per dose). The economic cost to receive one dose of the COVID-19 vaccine was $4.78. Findings can support the Government of Bangladesh to make efficient and equitable resource allocation decisions for vaccination programs.

Pricing and Replenishment Decision System Based on LSTM

Fresh supermarket vegetables have a very short shelf life, and the quality of vegetables will also be affected by the storage time. If most of the goods are not sold on that day, they will not be sold on the next day. From the demand side, the sales volume of vegetables is related to the supply side of vegetables from April to October. At the same time, the sales space of the supermarket is limited, which makes a reasonable sales portfolio extremely important. The commercial overpricing replenishment decision system based on LSTM analyzes the relationship between daily sales volume and cost-plus pricing for each vegetable category and single product over three years. It predicts the sales volume and wholesale price of each category for the next week, determining replenishment volume and pricing strategy based on forecasted sales volume and average loss rate. The system considers limited sales space and uses the entropy method to calculate weights for key indicators like total profit. It employs the TOPSIS method to sort items, considering forecasted wholesale prices, to select items for pricing decisions. The system aims to maximize total profit while ensuring the supermarket meets minimum display conditions. By assisting supermarkets in formulating more reasonable pricing strategies, it aims to reduce overstock, backlog, or stock shortages, ultimately improving sales volume and operational efficiency.

Automated pricing and replenishment forecasting for vegetable items

Firstly, the thesis extensively analysed and processed the vegetable sales data of a fresh food superstore. It integrated the product information and sales data of each vegetable category, and carried out data cleaning and analysis through Excel tools, including sorting out the outliers, listing the sales volume of each of the six categories, using pivot tables for statistical analysis, and establishing line graphs, heat maps, scatter plots, and other visual displays of the data characteristics and correlations. Secondly, the thesis investigated the relationship between sales volume and pricing, fitted the correlation between total sales volume and pricing using a random forest regression model, and predicted the daily replenishment volume for the coming week using an LSTM time series model. It also fitted quantitative fitting formulas for metrics for each vegetable category, classified profits into normal sales volume profits and discounted sales volume profits, and ultimately maximised profits through dynamic programming models and particle swarm optimisation algorithms. Finally, the thesis considered the impact of other factors such as weather, holidays, seasons and market environment on sales volume with specific data collection and analyses that help guide better pricing strategies. The findings will contribute to a better understanding of vegetable sales behaviour and optimise superstore operational strategies.

Seasonal and Weekly Patterns of Korean Adolescents' Web Search Activity on Insomnia: Retrospective Study.

Sleep deprivation in adolescents is a common but serious public health issue. Adolescents often have a progressive circadian delay and suffer from insufficient sleep during weekdays due to the school schedule. Temporal patterns in internet search activity data can provide relevant information for understanding the characteristic sleep problems of the adolescent population. We aimed to reveal whether adolescents exhibit distinct temporal seasonal and weekly patterns in internet search activity on insomnia compared to adults. We hypothesized that adolescents exhibit larger variations in the internet search volume for insomnia, particularly in association with the school schedule (e.g., academic vacations and weekends). We extracted the daily search volume for insomnia in South Korean adolescents (13-18 years old), adults (19-59 years old), and young adults (19-24 years old) during the years 2016-2019 using NAVER DataLab, the most popular search engine in South Korea. The daily search volume data for each group were normalized with the annual median of each group. The time series of the search volume was decomposed into slow fluctuation (over a year) and fast fluctuation (within a week) using fast Fourier transform. Next, we compared the normalized search volume across months in a year (slow fluctuation) and days in a week (fast fluctuation). In the annual trend, 2-way ANOVA revealed a significant (group) × (month) interaction (P<.001). Adolescents exhibited much greater seasonal variations across a year than the adult population (coefficient of variation=0.483 for adolescents vs 0.131 for adults). The search volume for insomnia in adolescents was notably higher in January, February, and August, which are academic vacation periods in South Korea (P<.001). In the weekly pattern, 2-way ANOVA revealed a significant (group) × (day) interaction (P<.001). Adolescents showed a considerably increased search volume on Sunday and Monday (P<.001) compared to adults. In contrast, young adults demonstrated seasonal and weekly patterns similar to adults. Adolescents demonstrate distinctive seasonal and weekly patterns in internet searches on insomnia (ie, increased search in vacation months and weekend-weekday transitions), which are closely associated with the school schedule. Adolescents' sleep concerns might be potentially affected by the disrupted daily routine and the delayed sleep phase during vacations and weekends. As we demonstrated, comparing various age groups in infodemiology and infoveillance data might be helpful in identifying distinctive features in vulnerable age groups.

B-310 Freezing and thawing serum without mixing prior to analysis causes false low Thyroglobulin results

Abstract Background Thyroglobulin (Tg), a glycoprotein that is synthesized in follicular cells of the thyroid gland, is the precursor of Thyroxin (T4) and Triiodothyronine (T3). Tg is a biomarker used for many clinical conditions, including monitoring for recurrence in patients with differentiated thyroid carcinoma. Typically, Tg levels drop to very low or undetectable levels after surgical excision and/or radioiodine ablation of the thyroid, with post-operative levels greater than 1-2 ng/ml indicating potential disease recurrence. Discordant Tg results in a patient sample were identified during a quality check of Tg results. This study investigates the root of cause of the discrepant Tg results. Given that: 1) Tg analyses were performed twice a week at our laboratory due to low daily test volume and patients’ samples received on an unanalytical day were stored at -20°C until analysis; 2) Tg analyses were performed by various medical technologists; 3) sample mixation prior to analysis was not routine practice for our automated laboratory, we hypothesized that the discrepancy was due to inconsistent sample handling, e.g., mixation vs unmixation prior to analysis. Methods Analysis of the daily QC and PT results in the period of the incident occurence ruled out causes in analytical phase. We split 52 de-identified patient serum samples into two equal aliquots and subsequently stored both aliquots at -20°C for 48 hours. Both aliquots of each sample were thawed at room temperature for 1 hour. One of the aliquots was analyzed for Tg without mixing, and the other was analyzed after mixing using a Vortexer for 4 seconds at 250 xg. To investigate if Tg concentrations form a gradient from the top to the bottom of a red-top vacutainer tube, five patient samples with Tg at 2.59, 2.79, 2.92, 52.37 and 144.12 ng/mL were thawed without mixing following storage at -20°C for 48 hours. Five layer (600 μl/layer) aliquots were carefully and sequentially taken from top to bottom for each specimen, and the Tg concentration in the aliquot of each layer was determined using the Beckman Coulter Access-2 Immunoassay. Results Of the 52 patients samples, 14 Tg results were excluded from statistical analysis due to exceeding the assay dynamic range (2 above and 12 below the upper and low limits, respectively). Analysis of the remaining 38 patient samples showed the mean of Tg concentrations was 22.8% (SD 18%) higher in mixed samples than the un-mixed samples. Regression analysis showed an R2 = 0.9976 (Mixed Tg = 1.1523 x Un-mixed + 0.4174, range 0.1 - 275.7 ng/mL, intercept 95%CI -0.721 to 1.556, slop 95%CI 1.1331 to 1.1715, p-value &amp;lt;0.001). Results of Tg gradient analysis showed significant higher Tg values in the 2nd, 3rd, 4th and 5th than the 1st layer by 26% (SD 11%), 63% (26%), 112% (57%), and 160% (76%), respectively. Conclusions False Tg values were found in samples without mixing before analysis after thawing from frozen state, due to formation of Tg concentration gradient in test tube. For obtaining reliable results, samples must be mixed before analysis when thawed from frozen storage.

The Influence of Personal Listening Devices on Hearing in Egyptian University Students: A Cross-Sectional Study

Background: Improper use of personal listening devices (PLDs) is associated with an increased risk of hearing loss. The purpose is to investigate the use of PLDs among university students and their impact on hearing. Methodology: A study involved 310 University students, surveyed on their PLDs habits. They shared their daily PLD use, volume level, and duration of use. Participants also discussed PLD-related hearing issues and other risk factors for hearing loss. Screening used the Five-minute hearing test (FMHT) and the “Hear WHO” app. Positive cases in both tests received diagnostic pure-tone audiometry (PTA). Results: About 70% used in-earpiece PLDs for studying, 53% used them for over 5 years. Also 83% used PLDs for over 60 mins daily, and 72% used them above volume level 6. Most (74%) were low-risk users. 10.6% tested positive for hearing loss in both screenings, with 1.6% confirmed by PTA. A shift in the 4 kHz average hearing threshold was noted. Tinnitus was reported by 49%, and ear pain by 42% of students. Higher risk PLD use predicted tinnitus, ear pain and excessive ear wax in multivariable analysis. Conclusions: Students' high-volume PLD use is linked to auditory problems like tinnitus and ear pain. Regular hearing screenings are recommended.

On the trail of CBCT-guided adaptive rectal boost radiotherapy, does daily delineation require a radiation oncologist?

IntroductionDose-escalation radiotherapy for rectal tumours is increasingly considered as a non-operative approach, with online-adaptive radiotherapy (oART) supporting this approach by correcting inter-fraction tumour position errors. However, using cone-beam computed tomography (CBCT)-guided oART requires daily target volume delineation by different operators, leading to inter-operator delineation variability and potential dosimetric issues. This study aims to compare and quantify the inter-operator and inter-professional delineation variability of the rectal boost volume on CBCT, including volumes by an automatically delineated oART treatment planning system. Materials and methodsA rectal boost volume, defined as the primary tumour extended to the entire adjacent rectal wall, was delineated on 10 CBCTs from 5 patients by 15 operators: 4 expert radiation oncologists (ROs), 4 radiation therapists (RTTs) and 7 non-expert ROs. These contours were compared between the different professional groups. A comparison to the average volume of the group (ROs, RTTs, or non-expert ROs) with the lowest delineation variability was also performed for each individual volume including the volume automatically generated by an oART treatment planning system. ResultsDelineation variability was the highest in the superior (range: 2.3–6.0 mm), and inferior (2.3–12.4 mm) directions, compared to the left (0.2–4.4 mm), right (0.3–2.0 mm), anterior (0.1–2.9 mm), and posterior (0.5–4.0 mm) directions. Non-expert ROs, RTTs, and automatic oART volume showed similar ranges of delineation errors when compared to the expert ROs’ volume, which was chosen as reference volume since this professional group showed the lowest variability. DiscussionExpert ROs showed consistent results. Other professional groups exhibit similar variability, comparable to the automatic oART volume. Therefore, RTTs could safely perform the rectal boost delineation without non-expert ROs supervision in the absence of expert ROs during CBCT-based oART. Moreover, these findings provide quantitative data to compute accurate margins for the rectal boost planning target volume in a CBCT-guided oART workflow.

OPTIMIZATION SYSTEM FOR LOCAL CONTROL OF COMPRESSOR STATION

The article was devoted to the characteristics of using a compressor to process gas deposits. It is an important principle that the liquid is lifted to the surface of the earth by the energy of the gas compressed from the layer during processing. The structure of the well, the diameter of the exploitation belt, and the fountain pipes should be determined in such a way that there is little pressure in the well and in the trunk. Fountain pipes should not be lowered from the barrier to reducing pressure in the well's trunk and saving metal consumption. Depending on the daily gas volume of oil deposits, the gradient of pressure in the bed and the duration of the gas-water border should have been known. The change in those parameters depends primarily on the energy of the bed. The exploitation of the oil bed can lead to gas and flooding. The gas tank is the force that enables the liquid to move. In the compressor regime, however, the main energy is the energy of six gases. The compressor lifts the liquid to the surface of the earth, mainly because of air or gas. Keywords: gas compressor, field operation compress, compressor characteristics, formation water, wellbore pressure.

Enhanced Foreign Exchange Volatility Forecasting Using Ceemdan with Optuna-Optimized Ensemble Deep Learning Model

Foreign Exchange (FX) is the largest financial market in the world, with a daily trading volume that significantly exceeds that of stock and futures markets. The prediction of FX volatility is a critical financial concern that has garnered significant attention from researchers and practitioners due to its far-reaching implications in the financial markets. This paper presents a novel hybrid ensemble forecasting model integrating a decomposition strategy and three deep learning (DL) models: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Convolutional Neural Network (CNN). This combination addresses individual models' limitations and further improves the accuracy and stability of FX volatility forecasting. The proposed approach utilizes the CEEMDAN technique to decompose volatility into multiple distinct intrinsic mode functions (IMFs) and merges these IMFs with GARCH and EGARCH volatilities to form the input dataset for the DL models. In addition, we employed an attention mechanism to improve the effectiveness of the DL techniques. Furthermore, the hyperparameters for the DL models are optimized using the Optuna algorithm. Finally, a hybrid ensemble model for forecasting exchange rate volatility is developed by combining the predictions of three distinct DL models. The proposed approach is evaluated against various benchmark models using evaluation measures such as MSE, MAE, HMSE, HMAE, RMSE, Q-LIKE, and the model confidence set (MCS) approach. The results demonstrate that our proposed approach provides accurate and reliable forecasts of FX volatility under different forecasting regimes, making it a valuable tool for financial practitioners and researchers.

Analyzing the effects of extreme cold waves on urban water supply network safety: A case study from 2020 to 2021

Urban water supply networks are crucial for the transportation of water resources. However, with the increasing frequency and severity of cold wave disasters linked to climate change, the impact on water supply systems has become a critical concern. These impacts include pipe failures, customer water outages, and challenges in meeting peak winter water demand. To address this, our study analyzes data from cold waves in Shanghai from late 2020 to early 2021. We statistically examined daily water supply volume, pressure, and pipe failures to detect abnormal changes. We also analyzed pipe failure rates based on different characteristics to identify which pipes are most vulnerable to cold wave damage. Understanding the winter cold wave's effects can help water companies prioritize maintenance on aging pipes before such events, reducing damage and improving service. This research adds to the understanding of climate change's impact on urban infrastructure and provides valuable insights for global water companies to optimize their maintenance strategies.

Utilizing textual data from online reviews for daily tourism demand forecasting: A deep learning approach leveraging word embedding techniques

Accurately estimating daily tourism volumes is crucial for optimizing operational strategies and enhancing visitor experiences at tourist destinations. In this study, we leverage historical tourism volume data, search engine data, and online review data (including textual review contents) to forecast daily tourism demand. We develop a deep learning framework that includes a feature selection module to select search engine indices, a word embedding module to transform review texts into numerical predictors, and an extreme learning machine (ELM) enhanced with the whale optimization algorithm (WOA) for predictive modeling. Based on different word embedding techniques, we investigated two specific forecasting methods: one based on term frequency-inverse document frequency (TF-IDF) and Transformer, and the other based on the standard pre-trained bidirectional encoder representations from Transformers (BERT). These two methods enhance predictive accuracy while ensuring fast training and inference speeds, making it suitable for the high-frequency daily tourism forecasting task. Experimental results demonstrate that the two methods significantly outperform traditional approaches that rely solely on numerical data sources, achieving lower prediction errors and faster inference speeds compared to established benchmark methods, highlighting their potential to contribute to advancements in tourism prediction methodologies.

Research Question : How do AI-Based Decision Support Systems Influence Managerial Decision-Making Processes in Businesses?

According to research by Oracle and Seth Stephens-Davidowitz, 85% of business leaders have experienced decision stress, and three-quarters have seen the daily volume of decisions they need to make increase tenfold over the last three years. As businesses keep growing rapidly, competition becomes tougher. Consequently, business leaders and managers are facing increasing pressures to come up with the right decisions for their business. In cases where vast amounts of data needs to be analyzed, there resides a high chance of patterns and information going unnoticed by humans. Consequently, Poor decision making is estimated to cost firms on average at least 3% of profits, which for a $5 billion company amounts to a loss of around $150 million each year. This may affect a likely business’ finances to a huge extent. Furthermore, analyzing such vast amounts of data is inefficient in relation to time and productivity. Consequently, This process also hinders business teams’ concentration on tasks aligned with their expertise.

Lymphatic embolization for early post-operative lymphatic leakage after radical cystectomy for bladder cancer.

Although radical cystectomy (RC) with pelvic lymph node dissection (PLND) is the standard treatment of muscle invasive bladder cancer, it may cause lymphatic leakage. Recent studies describe lymphatic embolization (LE) as an option to manage post-operative lymphatic leakage. Hence, this study evaluated the outcome of LE in patients receiving RC and analyzed factors associated with outcomes. This was a retrospective analysis of patients who underwent LE after RC for bladder cancer between August 2017 and June 2023. The data was assessed for analysis at January 2024. The patients were divided into a clinical success group and a clinical failure group. Clinical failure was defined as the following: 1) those who required drainage catheter placement >7 days after LE, 2) those who needed re-intervention before catheter removal, and 3) those who experienced adverse events associated with LE. Logistic regression analysis was performed to identify the factors associated with outcomes of LE. We analyzed 45 patients who underwent LE after RC. Twenty-eight (62.2%) patients were identified as clinically successful. Four patients required re-embolization, but none required more than two sessions of intervention. Three patients experienced lymphatic complications after LE. In multivariable analysis, maximal daily drainage volume of >1,000 mL/day (odds ratio [OR] = 4.729, 95% confidence interval [CI]: 1.018-21.974, p = 0.047) and diabetes mellitus (DM) (OR = 4.571, 95% CI: 1.128-18.510, p = 0.033) were factors associated with LE outcome. Our results suggest LE as a potentially effective procedure for controlling post-operative lymphatic leaks after RC, with few minor side effects. Patients exceeding a daily drainage of 1,000mL/day or with a medical history of DM have a higher risk for re-intervention and clinical failure after LE.

Operative Efficiency in Military Total Joint Arthroplasty Is Superior at an External Resource Sharing Agreement Facility as Compared to a Military Treatment Facility.

Surgeries within the Military Health System (MHS) are often performed by military physicians at External Resource Sharing Agreement (ERSA) hospitals to offload the operative demand at Military Treatment Facilities (MTFs). These agreements allow physicians flexibility in selecting where to treat military service members and other TRICARE beneficiaries. However, there is a paucity of military orthopedic literature comparing ERSA hospitals and MTFs. The objective of this study is to compare operative volume, efficiency, and orthopedic resident operative experience between an ERSA hospital and an MTF in military arthroplasty. A retrospective chart review was conducted for all surgeries performed between October 1, 2022 and July 31, 2023 by three military arthroplasty-trained surgeons at an MTF and two associated ERSA hospitals (denoted as ERSA-A and ERSA-B). Details recorded include spinal time-out time, spinal end time, in-room time, case start time, case stop time, and out-of-room time. These were used to calculate the total time in room (out-of-room time minus in-room time), case time (case stop time minus case start time), extra time in room (total time in room minus case time), and time between cases (in-room time minus out-of-room time of previous case). Lastly, we compared the average daily case volume and operative time between the facilities. Only primary joint replacements with properly documented time stamps were included in the efficiency analysis. Continuous variables were assessed for normality by the Shapiro-Wilk test. Non-normal data are presented as the median and interquartile range (IQR) and were analyzed using the Mann-Whitney U test (for two group comparisons) or the Kruskal-Wallis test (three or more groups) with Dwass-Steel-Chritchlow-Fligner pairwise comparisons. The associations between categorical variables were analyzed using the chi-squared test. Significance was set to P < .05. Statistical analyses were performed using SAS version 9.4 (Statistical Analysis Software, Cary, NC, USA). This study was classified as exempt human subjects research by the authors' institutional review board. Average daily case volume was significantly higher at both ERSA-A (median of 3, IQR 2-4, P < .001) and ERSA-B (median of 2, IQR 2-3, P = .0075) compared to the MTF (median of 2, IQR 1-2). This translated to residents operating for a median of 270 minutes (IQR 170-398, n = 25 OR days) each operative day at ERSA-A compared to 234 minutes (IQR 131-304, n = 91 OR days) when at the MTF, which was not a significant difference (P = .21). Median case time was 26.5 minutes lower (P < .001), extra time in room was 5 minutes lower (P < .001), and time between cases was 67 minutes lower (P < .001) in the ERSA group as compared to the MTF group. Utilizing ERSAs can improve patient throughput and operative efficiency, which in turn enhances orthopedic resident operative experience. These findings suggest that the MHS should seek ways to improve operative efficiency at MTFs. Expanding ERSA contracts to reach a broader portion of TRICARE beneficiaries, including patients over 65 years of age and those with supplemental insurance, who are currently ineligible, should also be explored.

Impact on beer sales of removing the pint serving size: An A-B-A reversal trial in pubs, bars, and restaurants in England.

Smaller serving sizes could contribute towards reducing alcohol consumption across populations and thereby decrease the risk of 7 cancers and other diseases. To our knowledge, the current study is the first to assess the impact on beer, lager, and cider sales (hereafter, for ease, referred to just as "beer sales") of removing the largest draught serving size (1 imperial pint) from the options available in licensed premises under real-word conditions. The study was conducted between February and May 2023, in 13 licensed premises in England. It used an A-B-A reversal design, set over 3 consecutive 4-weekly periods with "A" representing the nonintervention periods during which standard serving sizes were served, and "B" representing the intervention period when the largest serving size of draught beer (1 imperial pint (568 ml)) was removed from existing ranges so that the largest size available was two-thirds of a pint. Where two-third pints were not served, the intervention included introducing this serving size in conjunction with removing the pint serving size. The primary outcome was the mean daily volume of all beer sold, including draught, bottles, and cans (in ml), extracted from electronic sales data. Secondary outcomes were mean daily volume of wine sold (ml) and daily revenue (£). Thirteen premises completed the study, 12 of which did so per protocol and were included in the primary analysis. After adjusting for prespecified covariates, the intervention resulted in a mean daily change of -2,769 ml (95% CI [-4,188, -1,578] p < 0.001) or -9.7% (95% CI [-13.5%, -6.1%] in beer sold. The daily volume of wine sold increased during the intervention period by 232 ml (95% CI [13, 487], p = 0.035) or 7.2% (95% CI [0.4%, 14.5%]). Daily revenues decreased by 5.0% (95% CI [9.6%, -0.3%], p = 0.038). Removing the largest serving size (the imperial pint) for draught beer reduced the volume of beer sold. Given the potential of this intervention to reduce alcohol consumption, it merits consideration in alcohol control policies. ISRCTN.com ISRCTN18365249.