Randomized Evaluation Research Articles

Increasing screening for breast cancer using a randomized evaluation of electronic health record nudges: Design and rationale of the I-screen clinical trial.

Routine mammogram screening is critical for early detection of breast cancer. However, screening rates are below national targets, with persistent disparities among sub-populations. The purpose of this trial is to examine the effectiveness of a multi-component nudge intervention to increase breast cancer screening among eligible primary care patients. This is a two-phase, multisite, pragmatic cluster randomized clinical trial. In the first phase, two concurrent trials (trial A, B) will test the effectiveness of a multi-component nudge intervention that leverages the electronic health record (EHR) for increasing mammogram screening. The second phase includes a replication trial at an additional site. In Trial A, primary care clinics (n=30) will be randomized 2:1 to a nudge intervention or usual care. Eligible clinicians and patients within the intervention clinics will receive the intervention. Patients identified as high-risk for mammogram non-completion in the intervention arm will be further randomized 1:1 to receive an intensification nudge. In Trial B, primary care clinicians (n=82) will be randomized 1:1 to a nudge intervention or usual care. The primary outcome is mammogram completion within three months of the eligible visit. Outcomes will be evaluated using generalized estimating equations (GEE) to address clinic-level clustering. GEE models will also be fit to evaluate the impact of the intensification nudge on mammogram completion. If successful, this trial provides a blueprint for leveraging scalable multi-component nudge interventions for clinicians and patients to improve cancer screening rates.

Using Recombinase-Aid Amplification Combined with Pyrococcus furiosus Argonaute for Rapid Sex Identification in Flamingo (Phoenicopteridae)

Flamingos (Phoenicopteridae) are among the oldest birds worldwide and are loved by people for their bright red feathers. In addition, flamingos are sexually monomorphic birds, and distinguishing between males and females is difficult. The polymerase chain reaction (PCR) is widely used for sex identification. However, the PCR method requires a precise thermal cycler in the laboratory and is time-consuming. Therefore, developing a rapid, sensitive, and accurate method to identify the sex of flamingos is crucial. In this study, we established a sex identification system using a recombinase-aided amplification-Pyrococcus furiosus Argonaute (RAA-PfAgo) technique for greater flamingo (Phoenicopterus roseus). The greater flamingo-RAA-PfAgo system can identify unknown-sex greater flamingos in less than 1 h and can be visualized using a fluorescent detector or blue light. The results showed that optimal RAA-PfAgo conditions could detect 0.6 ng of genomic DNA and effectively differentiate between males and females. Random sample evaluations revealed that the system had a 100% coincidence rate compared with conventional PCR. In conclusion, this study provides a sensitive, specific, and accurate reference method for greater flamingo sexing.

A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques

Particulate Matter (PM) air pollution poses significant threats to public health. We introduce a novel machine learning methodology to predict PM2.5 levels at 30 m long segments along the roads and at a temporal scale of 10 seconds. A hybrid dataset was curated from an intensive PM campaign in Selly Oak, Birmingham, UK, utilizing citizen scientists and low-cost instruments strategically placed in static and mobile settings. Spatially resolved proxy variables, meteorological parameters, and PM properties were integrated, enabling a fine-grained analysis of PM2.5. Calibration involved three approaches: Standard Random Forest Regression, Sensor Transferability and Road Transferability Evaluations. This methodology significantly increased spatial resolution beyond what is possible with regulatory monitoring, thereby improving exposure assessments. The findings underscore the importance of machine learning approaches and citizen science in advancing our understanding of PM pollution, with a small number of participants significantly enhancing local air quality assessment for thousands of residents.

A novel framework for fatigue hotspot localization and damage assessment of multi-defect marine structures under random vibration

In ocean engineering, assessing vibration-induced fatigue under random loads is critical, particularly for ocean structures with multiple defects, as fatigue is highly sensitive to local structural flaws. Recently, incorporating structural dynamic characteristics into vibration fatigue assessments has emerged as a significant trend and formidable challenge, involving three key obstacles: precise localization of fatigue hotspots, extraction of key modes, and determination and integration of modal damage contributions. This paper introduces a novel framework for fatigue hotspot localization and damage assessment in the context of random vibration fatigue in ocean structures. By initially identifying fatigue hotspots and hot regions using stress mode shapes, refining the finite element mesh in hot regions, and conducting reanalysis, the exact locations of fatigue hotspots can be determined, enabling structural dimensionality reduction. The introduction of a modal damage contribution factor allows for the evaluation of each mode's damage contribution and the identification of key modes, further facilitating modal reduction. For the precisely localized fatigue hotspots, the proposed method combines the damage contributions of key modes, enabling rapid and accurate assessment of fatigue damage under random vibration loads. Finite element case studies and vibration fatigue test results demonstrate that the proposed framework effectively addresses the challenges of random vibration fatigue evaluation, achieving structural and modal reduction while significantly enhancing the efficiency of damage assessment without compromising accuracy.

Evaluation of patient and encounter decision aid interventions for atrial fibrillation: Baseline characteristics of the RED-AF study - A Randomized Controlled Trial

BackgroundThe Randomized Evaluation of Decision Support Interventions for Atrial Fibrillation (RED-AF) trial is a multi-site, randomized controlled clinical trial examining the effectiveness of a patient decision aid and an encounter decision aid in promoting shared decision-making (SDM) during a clinical encounter for patients with atrial fibrillation (AF). We sought to describe baseline characteristics of patients and clinicians in the trial and compare them to the demographics of the larger AF population. We also conducted an analysis of possible predictors of attrition rates at baseline, 6 and 12 months. MethodsThis study was a multi-center randomized controlled trial conducted at six academic centers across the U.S. Patients with non-valvular AF who qualify for anticoagulation therapy were eligible for enrollment. Patient demographics and characteristics were evaluated via questionnaires after their baseline clinical encounter. Participating clinicians completed demographic surveys, reporting educational background, specialty, and years of experience. Patient characteristics were analyzed via univariate logistic regression to identify potential trends among those lost to follow-up at each timepoint. FindingsA total of 1117 patients were enrolled in the RED-AF trial, with an average age of 69 (SD 9.3). Patients were predominantly male (61.7 %) and white (89.1 %), with 33.7 % reporting graduate or professional education. Clinicians (N = 107) were enrolled from specialties including cardiology (68.2 %), internal medicine (13.1 %), and pharmacy (14.0 %). No significant associations were found between any measured patient characteristics with survey completion at baseline, 6 or 12 months. ConclusionThe baseline demographics of the RED-AF trial reflect that patient participants were largely similar to prior studies investigating shared-decision making in patients with AF. The lack of association between patient demographics and attrition rates may highlight equity across the tested subgroups for survey completion for the study as a whole.

Preventive catheter ablation for ventricular arrhythmias in patients with end-stage heart failure referred for heart transplantation evaluation: Rationale for and design of the CASTLE-VT trial.

Timely referrals for transplantation and left ventricular assist device (LVAD) play a key role in favourable outcomes in patients with advanced heart failure (HF). Cardiovascular mortality, driven by sudden cardiac death, is the main reason for dying while waiting for heart transplantation (HTx). The purpose of the Preventive Catheter Ablation for ventricular arrhythmiaS in patients with end-sTage heart faiLure rEferred for heart transplantation eValuaTion (CASTLE-VT) trial is to test the hypothesis that prophylactic catheter ablation of arrhythmogenic ventricular scar tissue will reduce mortality, need for LVAD implantation, and urgent HTx in patients with end-stage HF related to ischaemic cardiomyopathy (ICM). CASTLE-VT is a randomized evaluation of prophylactic ablative treatment of arrhythmogenic ventricular scar in patients referred for HTx evaluation and diagnosed with ICM. Ablation will be performed with the use of a substrate-based approach in which the myocardial scar is mapped and ablated while the heart remains predominantly in sinus rhythm. The primary endpoint is the composite of all-cause mortality, worsening of HF requiring prioritized transplantation or LVAD implantation. The main secondary study endpoints are all-cause mortality, cardiovascular mortality, incidence of implantable cardioverter-defibrillator (ICD) therapy, hospitalizations, quality of life, time to first ICD therapy, number of device-detected ventricular tachycardia/ventricular fibrillation episodes, left ventricular function, and exercise tolerance. CASTLE-VT will randomize 160 patients with a follow-up period of 2 years. CASTLE-VT will determine whether prophylactic catheter ablation of arrhythmogenic ventricular scar tissue reduces mortality in patients with end-stage HF who are referred for HTx evaluation.

Prospective randomized evaluation of transcutaneous carbon dioxide monitoring during complex electrophysiological procedures under deep sedation: the TRACES trial.

The field of interventional electrophysiology is rapidly growing. For safe and efficient procedures sedation is needed for patient comfort and immobility. However, sedative and analgesic agents can induce respiratory depression, risking hypercapnia, hypoxia, and hypotension. This study evaluates the impact of adding transcutaneous carbon-dioxide (CO2) monitoring to standard monitoring practices in electrophysiological procedures under deep sedation. This prospective, randomized study at Ulm University Heart Center included 726 patients undergoing treatment under deep sedation between August 2019 and October 2023. Patients were randomly assigned to standard monitoring or standard monitoring with continuous transcutaneous CO2 monitoring (TCM-group). Standard monitoring involved non-invasive blood pressure, oxygen saturation (SpO2), and frequent peripheral venous blood gas analysis. The primary composite endpoint was SpO2 dips below 90% and pathological changes in venous blood gas analysis. The TCM-group included 357 patients, and the standard group had 348. The primary composite endpoint was reached by 195 patients (54.8%) in the TCM-group and 195 patients (56.5%) in the standard group (p = 0.642). However, peripheral venous CO2 partial pressure increased by more than 30% from baseline more often in the standard group than in the TCM-group (p = 0.005). Additionally, a venous pH below 7.25 was more common in the standard group (p = 0.047). While transcutaneous CO2 monitoring offers no significant benefit for the primary combined endpoint over standard monitoring during electrophysiological procedures, it helps mitigate significant CO2 elevations and respiratory acidosis. ClinicalTrials.gov, NCT04038476, 29 July 2019.

Improving learning outcomes for out-of-school children: evidence from a randomized evaluation of an accelerated learning program in Liberia

ABSTRACT Children in Liberia receive 2.2 learning-adjusted years of schooling, one of the lowest levels of educational attainment in the world. We study one approach to address low enrollment and low rates of learning: a 10-month accelerated learning program run by the Luminos Fund to help out-of-school children catch up to grade level. We conducted a randomized evaluation of the program across 100 communities in Liberia. We find that the program had large effects on reading and numeracy skills for out-of-school children, and that children in the program catch up to the learning levels of children in government schools.

Marker weighting improves single-step genomic prediction reliabilities of udder health traits in Nordic Red and Jersey dairy cattle populations

The standard single-step genomic prediction model assumes that all SNP markers explain an equal amount of genetic variance, which, however, may not be true. This is because SNPs are located in or near different genes with different functions. Therefore, it seems logical to consider SNP marker-specific weights when predicting genomic breeding values. We hypothesized that allowing differences in the amount of genetic variance explained by each SNP marker will improve prediction reliability and response to selection. To investigate this hypothesis, we first developed multi-trait standard single-step genomic models based on the current multi-trait random regression evaluation models for udder health traits of the Nordic Red (RDC) and Jersey (JER) dairy cattle populations. The models included 4 clinical mastitis (CM) traits, 3 test-day somatic cell score (SCS) traits, and the conformation traits fore udder attachment and udder depth. In the second step, we investigated the effect of applying different SNP marker weighting scenarios in the single-step genomic prediction models, for which a single-step SNP best linear unbiased prediction model was applied. We investigated the prediction reliability of the different models by forward prediction, where the last 4 years of the data were removed to estimate breeding values for validation candidates. In addition, genetic trends of the pedigree-based estimated breeding values (PEBV) and genomic enhanced breeding values (GEBV) were examined. The data sets for RDC and JER included 6.9 and 1.2 million animals of which 5.6 and 0.9 million cows had records, respectively. The number of genotyped animals was 125,789 and 64,777 for RDC and JER, respectively. Cows had repeated SCS observations but only single observations for all other traits and breeding values for all traits were modeled by one covariance function. This required modeling 12 eigenvalue breeding value coefficients for each cow and developing SNP marker weights for the principal components rather than for the biological traits. We investigated 3 SNP marker weighting scenarios: 1) a nonlinear method similar to BayesA, 2) using the classical formula 2pqû2 that accounts for allele heterozygosity, and 3) applying a mean SNP weight calculated by 2pqû2 for every 20 adjacent SNP markers. Bias, dispersion, and prediction reliability were calculated using PEBV or GEBV from the evaluation based on the full data set on those using the reduced data set. We found that the recent favorable genetic trend in CM and SCS has been accelerated since the introduction of genomic selection. The study also shows that a significant increase in prediction reliability, i.e., 0.74 vs. 0.48 for RDC and 0.72 vs. 0.41 for JER cows for CM, can be achieved with a standard single-step genomic prediction model compared with a pedigree-based prediction model. Almost all scenarios with SNP marker weighting further improved the prediction reliability between 0.5% and 12.7%. The highest improvement was achieved by weighing the SNP markers based on the 2pqû2 formula.

Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists’ ultrasound scanning for regional anesthesia

ObjectivesUltrasound-guided regional anesthesia (UGRA) relies on acquiring and interpreting an appropriate view of sonoanatomy. Artificial intelligence (AI) has the potential to aid this by applying a color overlay to key...

Low-carbon information quality dimensions and random forest algorithm evaluation model in digital marketing

The growing urgency for low-carbon lifestyles necessitates developing effective strategies to promote sustainable consumer choices. This study investigates key dimensions of information quality that shape consumer behavior within digital marketing to achieve this goal. Employing a mixed-methods approach that integrates grounded theory and machine learning, this study identifies three core dimensions of low-carbon information quality: matching quality, presentation quality, and interpretability quality. These dimensions underscore the importance of aligning information with consumer needs, ensuring clear and accurate presentation, and fostering transparency for trustworthiness. A Random Forest algorithm-based evaluation model is constructed to assess low-carbon information quality, demonstrating its effectiveness in identifying high-quality, sustainable content. This research provides a practical tool for digital marketers to enhance their strategies, raise consumer awareness of sustainable options, and ultimately contribute to the growth of the low-carbon consumption market.

Analysis on pulse rate variability for pilot workload assessment based on wearable sensor

AbstractThe workload levels of pilots directly affect their flight performance and the safety of the whole flight. To explore the real‐time workload of pilots in different flight phases (takeoff, cruise, and landing), this paper leveraged National Aeronautics and Space Administration Task Load Index (NASA‐TLX), a subjective evaluation scale, and PhotoPlethysmoGraphy (PPG) signals of 21 participants using a flight simulator and a wearable sensor. First, the workloads of pilots under different phases were explored by the NASA‐TLX scales; secondly, the pulse rate variability (PRV) features were selected by variance analysis and random forest importance evaluation; finally, the performances of the k‐nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) were compared for workload levels identification. It is shown that the workloads are ranked as follows: landing > takeoff > cruise. SDNN, CVCD, CVNNI, LF, TP, SD2, and SD2/SD1 were used as selected features with significant differences in different flight phases. In addition, machine learning models can effectively identify pilot workloads, and feature selection enhances the performance of both KNN and RF classifiers. The best identification of workload was achieved using the selected PRV features as inputs to the KNN classifier, with an average accuracy of 88.9%. Our results indicate that the KNN classifier and PRV features are suitable for identifying pilot workload. The pilot workload is highest during the landing phase, which provides a reference for flight safety management. The findings from this research could contribute to developing a robust pilot workload detection system and improve current flight operation safety regulations.

Topography Modeling of Surface Grinding Based on Random Abrasives and Performance Evaluation

The surface morphology and roughness of a workpiece are crucial parameters in grinding processes. Accurate prediction of these parameters is essential for maintaining the workpiece’s surface integrity. However, the randomness of abrasive grain shapes and workpiece surface formation behaviors poses significant challenges, and accuracy in current physical mechanism-based predictive models is needed. To address this problem, by using the random plane method and accounting for the random morphology and distribution of abrasive grains, this paper proposes a novel method to model CBN grinding wheels and predict workpiece surface roughness. First, a kinematic model of a single abrasive grain is developed to accurately capture the three-dimensional morphology of the grinding wheel. Next, by formulating an elastic deformation and formation model of the workpiece surface based on Hertz theory, the variation in grinding arc length at different grinding depths is revealed. Subsequently, a predictive model for the surface morphology of the workpiece ground by a single abrasive grain is devised. This model integrates the normal distribution model of abrasive grain size and the spatial distribution model of abrasive grain positions, to elucidate how the circumferential and axial distribution of abrasive grains influences workpiece surface formation. Lastly, by integrating the dynamic effective abrasive grain model, a predictive model for the surface morphology and roughness of the grinding wheel is established. To examine the impact of changing the grit size of the grinding wheel and grinding depth on workpiece surface roughness, and to validate the accuracy of the model, experiments are conducted. Results indicate that the predicted three-dimensional morphology of the grinding wheel and workpiece surfaces closely matches the actual grinding wheel and ground workpiece surfaces, with surface roughness prediction deviations as small as 2.3%.

Jianpi Qingre Tongluo Decoction exerted an anti-inflammatory effect on AS by inhibiting the NONHSAT227927.1/JAK2/STAT3 axis

PurposeThis study aims to determine whether Jianpi Qingre Tongluo Decoction (JQP) alleviates ankylosing spondylitis (AS) inflammation via the NONHSAT227927.1/JAK2/STAT3 axis. MethodsThe effect of JQP on immune-inflammatory indicators in AS patients was explored through a combination of data mining, association rule analysis, and random walk model evaluation. Subsequently, network pharmacology and molecular docking were performed to screen out the potential signaling pathway. ELISA, PCR and wb were used to evaluate the effect of JQP on AS-FLS activity and inflammatory factors. The role of NONHSAT227927.1/JAK2/STAT3 combination in inflammation was studied by editing NONHSAT227927.1 and adding the JAK2/STAT3 inhibitor AG490. Involvement of the JAK2/STAT3 pathway was detected by PCR, WB, or immunofluorescence analysis. ResultsRetrospective data mining results show that JQP can effectively reduce the immune inflammatory response in AS patients. Through network pharmacology and molecular docking, it is speculated that JQP exerts its effect on AS through the JAK2/STAT3 pathway. Overexpression of NONHSAT227927.1 activated the JAK2/STAT3 pathway and promoted the expression of inflammatory factors, while serum containing JQP reversed the effects of NONHSAT227927.1 overexpression. NONHSAT227927.1 silencing inhibits the proliferation of AS-FLSs, inhibits the levels of inflammatory factors, and reduces the expression of JAK2/STAT3 protein. After adding the pathway blocker AG490, it was observed that the cell viability of AS-FLSs was reduced by inflammatory factors and the levels of JAK2/STAT3 were inhibited. , and overexpression of NONHSAT227927.1 can reverse this trend. ConclusionsJQP exerted an anti-inflammatory effect on AS by inhibiting the NONHSAT227927.1/JAK2/STAT3 axis.

Technology and the state: Building capacity to tax via text

The ability of the state to collect tax revenue is a crucial indicator in the process of economic development, yet the functioning of tax compliance in low-capacity countries remains poorly understood. Using a randomized evaluation, I study a simple text message reminder scheme implemented by the Uganda Revenue Authority, which increases tax compliance by 7%. This average effect masks substantial treatment effect heterogeneity by an index of the presence of public services, measured via a granular, nationwide dataset on government service provision. The finding that the treatment was most effective where the state is least present shows how digital technology can extend beyond the brick-and-mortar presence of the government, expanding the reach of the state.

#2801 Which patients may benefit most from hemodiafiltration compared with hemodialysis: prediction of the individualized treatment effect

Abstract Background and Aims Haemodiafiltration (HDF) with an appropriate dosing reduces death from any cause based on an individual participant data meta-analysis and aggregated data meta-analyses of available randomized trials. No differences in treatment effect are reported for specific patient populations when defined on a single characteristic in subgroup analyses. Yet, it is unclear to what extent individual patients, characterized by a combination of clinical characteristics, may benefit from HDF. This study aimed to develop and internally validate a treatment effect prediction model to determine which individual patients would benefit most from well dosed HDF, compared with haemodialysis (HD), in terms of survival. Method Individual participant data from five randomized controlled trials (CONTRAST, ESHOL, Turkish HDF study, French HDF study, CONVINCE) comparing HDF with HD on all-cause mortality were used to derive a Royston-Parmar model for exploring the prediction of absolute treatment effect of HDF based on pre-specified patient and disease characteristics, notably age, sex, body mass index, diabetes mellitus, history of cardiovascular disease, creatinine levels, and c-reactive protein levels. Internal validation of the model was performed using internal-external cross validation. Results Among 4153 participants, with a median follow-up of 30 months (Q1-Q3: 24-36), death from any cause occurred in 558 patients (27.2%). The median predicted survival benefit of HDF compared with HD was 6.9 (Q1–Q3: 5.6–9.0) months, with a range of 2 to 42 months. Patients who were predicted to benefit most from HDF were younger, less likely to have diabetes or a cardiovascular history and had higher serum creatinine levels. Internal-external cross validation showed adequate discrimination and calibration. Conclusion All-cause mortality is reduced by HDF compared with HD in ESKD patients. Yet, the absolute survival benefit seems to considerably differ between individual patients. Our results suggest that the effects of HDF on absolute survival can be predicted accurately using a combination of readily available patient and disease characteristics. This prediction model approach exemplifies the potential of prediction algorithms in supporting clinical decision-making for the choice of treatment options. Validation and potential updates to the presented algorithm are necessary, along with a preferable randomized evaluation of its clinical impact before widespread implementation.

A structured approach to modifying an implementation package while scaling up a complex evidence-based practice.

To describe a structured, iterative, data-driven approach for modifying implementation strategies for a complex evidence-based practice during a nationwide scale-up initiative. We scaled-up implementation of Critical Time Intervention (CTI)-an evidence-based case management model-across 32 diverse community-based Veterans Affairs (VA) "Grant and Per Diem" case management (GPD-CM) agencies that serve homeless-experienced Veterans transitioning to independent living. Primary data were collected using qualitative methods. We embarked on a scale-up initiative while conducting a pragmatic randomized evaluation using a roll-out design, comparing two versions of a CTI implementation package tailored to VA's GPD-CM program. We iteratively assessed contextual factors and implementation outcomes (e.g., acceptability); findings informed package modifications that were characterized using the Framework for Reporting Adaptations and Modifications to Evidence-based Implementation Strategies. We conducted semi-structured interviews with Veterans, GPD-CM staff, and liaising VA clinicians; periodic reflections with liaising VA clinicians and implementation team members; and drew upon detailed meeting notes. We used rapid qualitative methods and content analysis to integrate data and characterize modifications. After each scale-up wave-in response to variations in agency-level characteristics- we made iterative modifications to the implementation package to increase CTI adoption and fidelity across the diverse contexts of our scale-up sites. Modifications included adding, deleting, integrating, and altering the package; core package components were preserved. Implementation packages for complex evidence-based practices undergoing scale-up in diverse contexts may benefit from iterative modifications to optimize practice adoption with fidelity. We offer a structured, pragmatic approach for iteratively identifying data-driven, midstream implementation package adjustments, for use in both VA and non-VA scale-up initiatives. Our project demonstrates the importance of assessing for and making modifications in a scale-up initiative, as well as the trade-offs of projects having simultaneous formative and summative evaluation aims.

Employing machine learning for enhanced abdominal fat prediction in cavitation post-treatment

This study investigates the application of cavitation in non-invasive abdominal fat reduction and body contouring, a topic of considerable interest in the medical and aesthetic fields. We explore the potential of cavitation to alter abdominal fat composition and delve into the optimization of fat prediction models using advanced hyperparameter optimization techniques, Hyperopt and Optuna. Our objective is to enhance the predictive accuracy of abdominal fat dynamics post-cavitation treatment. Employing a robust dataset with abdominal fat measurements and cavitation treatment parameters, we evaluate the efficacy of our approach through regression analysis. The performance of Hyperopt and Optuna regression models is assessed using metrics such as mean squared error, mean absolute error, and R-squared score. Our results reveal that both models exhibit strong predictive capabilities, with R-squared scores reaching 94.12% and 94.11% for post-treatment visceral fat, and 71.15% and 70.48% for post-treatment subcutaneous fat predictions, respectively. Additionally, we investigate feature selection techniques to pinpoint critical predictors within the fat prediction models. Techniques including F-value selection, mutual information, recursive feature elimination with logistic regression and random forests, variance thresholding, and feature importance evaluation are utilized. The analysis identifies key features such as BMI, waist circumference, and pretreatment fat levels as significant predictors of post-treatment fat outcomes. Our findings underscore the effectiveness of hyperparameter optimization in refining fat prediction models and offer valuable insights for the advancement of non-invasive fat reduction methods. This research holds important implications for both the scientific community and clinical practitioners, paving the way for improved treatment strategies in the realm of body contouring.

Who Uses Childcare Centers? Evidence from Burkina Faso

What determines childcare center use for low-income urban households? And how does subsidizing provision change access to childcare services? We study these questions using data from a randomized evaluation that subsidized childcare center use for women participating in a public works program in Burkina Faso. One quarter of eligible women use these childcare services, tripling childcare center use for zero-to six-year-olds in our sample. Household wealth significantly predicts childcare center use. However, the public works childcare centers increased access for disadvantaged households and young children. Women employed as childcare providers were also more likely to use the childcare centers.

Light as a drug: prospective randomized evaluation and comparison of the effect of decreased illumination on visual recovery after cataract surgery.

To compare the effect of decreased illumination on the rate of postoperative visual recovery, and the incidence of cystoid macular edema (CME) with surgical visualization achieved with a traditional analog operating microscope compared with a 3D digital visualization system. Ambulatory surgery center, New York. Prospective, randomized, consecutive, single-surgeon series. Patients undergoing routine cataract surgery were randomized into either (1) visualization through the binoculars of a standard operating microscope ("traditional group") or (2) visualization through a 3D digital visualization system affixed to the same operating microscope ("digital group"). Note was made in each case of light intensity used, light exposure time, cumulative dissipated energy (CDE), femtosecond laser use, preoperative medical and ocular conditions, intraoperative and/or postoperative complications, and preoperative and postoperative visual acuities and optical coherence tomography confirmed CME. The study comprised 118 eyes in the traditional group and 96 eyes in the digital group. There were no differences in preoperative visual acuity, light exposure time, CDE, or femtosecond laser use between groups, but the light intensity used in the digital group was significantly less (19.5% ± 0.5%) than in the traditional group (48.6% ± 0.6%; P < .001). Furthermore, the digital group achieved a better decimal postoperative day 1 visual acuity (0.60 ± 0.03) with less rates of CME (2.1%) when compared with that of the traditional group (0.51 ± 0.02, P = .03; and 9.2%, P = .03), respectively. Visual recovery and CME rates were significantly better in patients who underwent cataract surgery assisted by the 3D digital visualization platform without an increase in complications or surgical time.