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Impact of bariatric surgery on monthly earnings and employment:a national linked data study in England, 2014-2022
Get PDFBACKGROUND/OBJECTIVE: There is evidence that living with obesity can affect an individual's pay and employment, but there is little evidence on the impact of weight-management interventions in improving labour market outcomes of individuals. We evaluate the impact of bariatric surgery on monthly earnings and employee status among working-age adults, and examine variations across sociodemographic characteristics. METHODS: This population-based, retrospective longitudinal cohort study for England included 40,662 individuals who had a bariatric surgery procedure and obesity diagnosis between 1 April 2014 and 31 December 2022, with no bariatric surgery history in the previous 5 years, and were 25 to 64 years old at the date of surgery. 49,921 individuals sampled from the general population who had not had bariatric surgery were also included, matched by age and sex. The main outcome measures were monthly employee pay-for all months and only months where the individual was in paid employment-expressed in 2023 prices and paid employee status. RESULTS: Among people living with obesity who had bariatric surgery, there was a sustained increase in monthly employee pay from 6 months after surgery with a mean increase of £84 per month (95% confidence interval [Cl]: 63-106) 5 years after surgery compared with the 6 months before surgery. There was a sustained increase in the probability of being a paid employee from 4 months after bariatric surgery, with a mean increase of 4.3 percentage points (95% Cl: 3.7-4.9) 5 years after surgery. CONCLUSION: Bariatric surgery is associated with an increased probability of being employed, resulting in increased earnings. This suggests that living with obesity negatively impacts labour market outcomes and that obesity management interventions are likely to generate economic benefits both to individuals and on a macroeconomic level by increasing the likelihood of employment of people living with obesity
Qualitative evidence synthesis
Get PDFQualitative evidence synthesis (QES) offers a method for presenting patients’ attitudes, beliefs, and experiences from multiple qualitative studies. It is used in health technology assessments (HTAs) to understand diverse patient perspectives alongside quantitative evidence of clinical and cost effectiveness. QES methods include meta-aggregation, thematic synthesis, meta-ethnography, and framework synthesis. Factors like research question, available resources, expertise, and data types guide the choice of QES method. QES involves formulating the review question, literature searching, quality assessment, analysis, and synthesis. Dissemination considers the intended audience and purpose. Approaches to integrate qualitative and quantitative data include using mixed-methods synthesis methodologies, conceptual frameworks, or logic models. QES is growing rapidly due to increased recognition of decision-making complexities and the role of patient-clinician interactions in technology effectiveness. Limitations include quality of primary study reporting and interpretive nature. Innovations like GRADE-CERQual offer opportunities for incorporating synthesised patient perspectives in HTAs and decision-making
Polymer-supported manganese-catalysed transformation of esters and aldehydes to alcohols
Get PDFCompared to precious metals, manganese has emerged as a more earth-abundant and potentially safer metal to catalyse important chemical transformations. In contrast to homogeneous Mn catalysts, the use of heterogeneous Mn catalysts, is rare. Herein we describe the preparation of a Mn catalyst supported on a simple phosphine-containing polymer, that efficiently provides alcohols from esters and aldehydes, and facilitates the reduction of amides. Surprisingly, the catalyst gave improved yields upon recycling, which prompted a short study into the structural and oxidative changes of the catalyst using SEM-EDX and XAFS. Breakage of the microspheres and increased Mn oxidation states were observed upon recycling. Ultimately a priming procedure was developed that enabled direct procurement of an active and viable catalyst system
Sustainable extraction of bioactive compounds: a life cycle perspective on technologies, solvents, and process scale-up
Get PDFThe extraction of bioactive natural compounds is crucial to the pharmaceutical, food, and cosmetic industries, but it often entails high energy consumption, greenhouse gas emissions, and environmental impacts associated with solvent use. Life Cycle Assessment (LCA) provides a structured approach to identify environmental hotspots and evaluate trade-offs across solvent use, energy demand, and process scale-up. This review adopts a life-cycle perspective to examine the environmental impacts from upstream stages, including agriculture, raw material processing, and transportation, to extraction, waste management, and end-of-life treatment. Extraction technologies, including microwave-assisted, ultrasound-assisted, solvent-based, pressurized liquid, and high-voltage electrical discharge methods, are compared in terms of environmental performance and process efficiency. Solvent selection is highlighted as a critical factor, with a focus on the balance between extraction yield and sustainability across water, organic, and deep eutectic solvents. The integration of LCA with simulation tools, such as SuperPro Designer and Aspen Plus, is also reviewed for its potential to support scaling-up decisions and resource optimization. Although current LCA studies provide valuable insights, gaps remain in addressing energy constraints, waste flows, and real-world implementation. Advancing sustainable extraction requires a combination of system-level design, data-driven modeling, and circular resource utilization
Prediction models for incident stroke in the community:a systematic review and meta-analysis of predictive performance
Get PDFAIMS: Stroke is the second leading cause of death and the third leading cause of disability worldwide. We performed a systematic review and meta-analysis of multivariable models applicable to the prediction of incident stroke in community cohorts. METHODS AND RESULTS: Ovid Medline and Embase were searched for studies related to stroke and prediction models from inception to 3 November 2025. Measures of discrimination were extracted and pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool and certainty in effect estimates by Grading of Recommendations, Assessment, Development and Evaluation. Forty-one studies met the inclusion criteria, describing 80 prediction models, with two (R-FSRS and Basic IS) eligible for meta-analysis, including 969 514 participants. Both R-FSRS (summary c-statistic 0.714, 95% CI 0.681-0.747) and Basic IS (0.709, 95% CI 0.647-0.769) showed acceptable discrimination performance. Risk of bias was high in 66% of models, and both models showed reduced performance when excluding development cohorts and studies at high risk of bias (R-FSRS, 0.667, 95% CI 0.604-0.727; Basic IS 0.701; 95% CI 0.583-0.807). Only 43% of studies reported calibration, and no model underwent clinical utility analysis or a clinical impact study. CONCLUSION: Many models have been derived for stroke prediction, however, they are rarely externally validated, and studies are limited by a high risk of bias, poor reporting of calibration and a lack of clinical utility analysis or prospective validation. Thus, the evidence base is insufficient to translate these models to clinical practice
UrbanMFM: Spatial Graph-Based Multiscale Foundation Models for Learning Generalized Urban Representation
Get PDFAs geospatial data from web platforms becomes increasingly accessible and regularly updated, urban representation learning has emerged as a critical research area for advancing urban planning. Recent studies have developed foundation model-based algorithms to leverage this data for various urban-related downstream tasks. However, current research has inadequately explored deep integration strategies for multiscale, multimodal urban data in the context of urban foundation models. This gap arises primarily because the relationships between micro-scale (e.g., individual points of interest and street view imagery) and macro-scale (e.g., region-wide satellite imagery) urban features are inherently implicit and highly complex, making traditional interaction modeling insufficient. This paper introduces a novel research problem – how to learn multiscale urban representations by integrating diverse geographic data modalities and modeling complex multimodal relationships across different spatial scales. To address this significant challenge, we propose UrbanMFM, a spatial graph-based multiscale foundation model framework explicitly designed to capture and leverage these intricate relationships. UrbanMFM utilizes a self-supervised learning paradigm that integrates diverse geographic data modalities, including POI data and urban imagery, through novel contrastive learning objectives and advanced sampling techniques. By explicitly modeling spatial graphs to represent complex multiscale urban relationships, UrbanMFM effectively facilitates deep interactions between multimodal data sources. Extensive experiments on datasets from Singapore, New York, and Beijing demonstrate that UrbanMFM outperforms the strongest baselines significantly in four representative downstream tasks. By effectively modeling spatial hierarchies with diverse data, UrbanMFM provides a more comprehensive and adaptable representation of urban environments
Relationships between teaching mode and mental and physical health outcomes of Canadian students between October 2020 and September 2022
No full textThe COVID-19 Pandemic prompted a rapid shift from in-person to remote learning, disrupting students’ academic experiences as well as their mental and physical health. The impact of teaching mode on these outcomes, particularly among Canadian postsecondary students, remains underexplored. This study examined relationships between teaching mode and Canadian postsecondary students’ academic experiences, mental health, and health behaviours during the first two years of the pandemic. Data came from the International COVID-19 Awareness and Responses Evaluation (iCARE) study, a multi-wave cross-sectional survey. This analysis included 2164 postsecondary students reporting online (n = 963), hybrid (n = 649), or in-person (n = 413) learning, with data collected between 29 October 2020, and 13 September 2022. Selected questions assessed academic experiences, mental health, and health behaviours. Descriptive statistics and multivariable logistic regression were used to examine associations with teaching mode. Online students reported the poorest mental health outcomes, greatest declines in physical activity, and increased alcohol use. Hybrid students faced the greatest academic-related declines. In-person students reported higher satisfaction with school but poorer dietary habits and increased e-cigarette use. Online students also anticipated greater benefits from in-person learning than in-person students reported. Challenges were more pronounced among females, lower-income students, and those with pre-existing health conditions. Teaching mode appeared to significantly influence student well-being during the pandemic, with online students reporting the worst overall outcomes. However, these effects are difficult to isolate given the variation in home and social environments. Findings highlight the need for targeted supports across learning formats and longitudinal research examining how these outcomes evolve over time
The Cadenza lyric intelligibility prediction (CLIP) dataset
Get PDFThis paper presents CLIP, a dataset of 11,072 popular western music signals sourced from independent artists, accompanied by ground truth lyrics, and lyric intelligibility scores from listening tests. The dataset is designed to facilitate music information retrieval (MIR) research using machine learning. It was created to allow the development of algorithms to predict lyric intelligibility for the Cadenza ICASSP 2026 Signal Processing Grand Challenge. Currently, it is the only publicly available large-scale dataset for such a task. The music was sourced from the Free Music Archive (FMA) dataset and is unlikely to be familiar to listeners. We excluded tracks whose license did not allow derivative works and those that did not have English singing. Ground truth transcriptions were generated by seven native English speakers, resulting in 3700 excerpts of 5 to 10 words each from 1452 different songs. A hearing loss simulation was also applied to the stereo audio. This resulted in 11,100 music signals with no, mild or moderate hearing loss. This was done so more diverse hearing is represented in the dataset. Human transcriptions were then collected via an online listening experiment. Participants self-reported as having normal-hearing and being native English speakers. They listened to each music signal twice before transcribing each line. Final intelligibility scores were the ratio of matching words between the listening test responses and the ground truth transcriptions. The final dataset consists of audio, ground truth lyrics, intelligibility scores and associated metadata
Optimization under attack: Resilience, vulnerability, and the path to collapse
Get PDFOptimization is critical for improving the operations of large-scale socio-technical infrastructures such as those found in energy, mobility, and information systems. In particular, understanding the performance of multi-agent discrete-choice combinatorial optimization under distributed adversarial attacks is a compelling and underexplored problem. Multi-agent systems involve a large number of remote control variables that can influence the cost-effectiveness of distributed optimization heuristics. This paper unravels, for the first time, the trajectories of distributed optimization from resilience to vulnerability, and finally to collapse under varying adversarial influence. Using real-world and synthetic data to generate over 112 million multi-agent optimization scenarios, we systematically assess how the number of agents with varying levels of adversarial severity and network positioning influences optimization performance, with particular attention to the impact on Pareto optimality. With this large-scale dataset, made openly available as a benchmark, we disentangle how optimization systems remain resilient to adversaries and which adversary conditions make optimization vulnerable or cause collapse. These findings can support the design of self-healing strategies for fault tolerance and fault correction, addressing a critical gap in adversarial distributed optimization
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