International Journal of Population Data Science最新文献

{"title":"Improving community access to data and analytic services: an approach based on needs assessment and co-creation.","authors":"Elise Leong-Sit, Laura Ferreira-Legere, Bilal Khan, Susan Bronskill, J Michael Paterson, Lesley Plumptre, Minnie Ho","doi":"10.23889/ijpds.v11i5.3638","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3638","url":null,"abstract":"<p><strong>Objective: </strong>Communities who are the subject of administrative health and demographic data often encounter challenges accessing their own data and related analytic services, but achieving equitable access to data and analytic services is emerging as a crucial facet of ethical data stewardship, enabling communities to directly translate insights into real-world impact. As part of its mandate as a data institute, ICES offers organizations in Ontario (Canada) access to data analytics to support service planning and evaluation. Though community organizations are eligible, they accounted for only 2.4% of projects before 2023. To address this gap, we launched a community engagement and co-development process to increase service awareness and simplify access.</p><p><strong>Approach: </strong>We conducted a two-pronged needs assessment: environmental scan and seven key informant interviews based on purposive sampling. We then co-developed promotional materials with two other community members, and pilot-tested drafts with focus groups that included a mix of previous and new key informants (eight in total).</p><p><strong>Results: </strong>The needs assessment revealed the importance of developing materials using plain language, storytelling, messaging on impact, and trusted messengers. Products developed included a testimonial video showcasing previous community projects, a plain language webpage, and a 3-year dissemination strategy centered on building trusting relationships.</p><p><strong>Conclusion: </strong>Intentional needs assessment and embedded co-design approaches revealed the importance of narrative, plain, and impact-focused communication, leading to a novel suite of communication tools. Implications: Ongoing evaluation of these new approaches will be used to continue to refine promotion strategies as well as potentially tailor or expand future services.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3638"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Context Integration in Population-Scale Social Networks and Its Association With Mortality.","authors":"Yue Li, Gerald Mollenhorst, Rense Corten, Marco Helbich","doi":"10.23889/ijpds.v11i5.3708","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3708","url":null,"abstract":"<p><p>Research on social networks and mortality has shown that structural characteristics of individuals' social environments are associated with health outcomes, yet most empirical work has relied on egocentric or survey-based measures that capture selected parts of people's networks. In this study, we use Dutch population registers to construct nationwide multilayer social networks that link individuals through family, household, workplace, educational, and neighborhood contexts. These data allow us to derive yearly individual-level indices-cross-context integration (excess closure across layers), density (mean embeddedness), closeness centrality, network heterogeneity, and network size-which reflect different dimensions of social structure. We link these indices to annual mortality records and estimate associations using time-varying discrete-time survival models with person-year observations. We also compare pre-COVID and COVID periods to explore whether associations vary under changing environmental risk conditions. By leveraging registry-based multilayer networks, this study provides new evidence on how multiple structural dimensions of social environments are patterned in relation to mortality risk at the population level.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3708"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426676/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility research into using multiple administrative data sources and predictive modelling to produce disability status estimates for England.","authors":"Matthew Minifie, Charlotte Standeven, Jesse Ransley, Sarah Wood","doi":"10.23889/ijpds.v11i5.3485","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3485","url":null,"abstract":"<p><p>Official estimates of population by disability status for England and Wales are produced as part of the census once every ten years, but there is a pressing user need for more frequent, robust disability statistics. This presentation outlines ongoing novel research that responds to that challenge by examining the feasibility of producing population-level estimates for disability status in England using a predictive model applied to a suite of linked administrative data. We have linked nine administrative data sources from different government departments, including benefits, education, employment and health datasets. The de-identified data are linked via a population spine (the England-based residents on the Office for National Statistics' 2021 Statistical Population Dataset with a valid disability status from Census 2021), with a sample size of 48.9 million. Machine learning algorithms were then used to train a model on a subsample of data. The binary outcome variable is Census 2021 disability status (non-disabled or disabled). Predictor variables include age, sex, geography and various benefit, education, employment and health variables from the administrative data. The performance of the model was tested and evaluated on a different subsample of the data to that on which the model was trained. Various evaluation metrics were employed to assess the performance of the model, along with predicted and observed disability prevalence rates by socio-demographic factors to further assess the coherence between the predictive model and Census 2021. Additionally, the predictive model was applied to earlier years for limited analysis of a short time series.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3485"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426549/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing a Scalable, Secure Genomics Ingest and Processing Pipeline in a Trusted Research Environment for Dementia Research.","authors":"Alieyeh Saraband Moghaddam, Lewis Hotchkiss, Emma Squires, Simon Thompson","doi":"10.23889/ijpds.v11i5.3709","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3709","url":null,"abstract":"<p><p>The integration of genomics data into dementia research offers transformative potential for understanding disease mechanisms and enabling precision medicine. However, its utility is constrained by significant challenges in data sharing, privacy, standardization, and computational scale. To address these barriers, we designed and implemented a secure, novel and scalable genomics ingest and processing pipeline within the Dementias Platform UK (DPUK) Trusted Research Environment. Our comprehensive, end-to-end framework encompasses a seven-stage process, including cohort discovery via an interactive metadata matrix, rigorous quality and privacy controls using bioinformatic tools (e.g. PLINK, VCFtools, etc.), a genomic data organization standard for scalability and balance between interoperability and flexibility, as well as secure provisioning within a Five Safes governance model. A key innovation is the use of a MinIO-based object storage architecture with custom metadata tagging, enabling efficient, privacy-preserving data access at large scale. We also operationalized a standardized polygenic risk score (PRS) pipeline to generate validated, derived datasets. This integrated system now facilitates secure research access to harmonized genomic data across 15 diverse cohorts, including population-based, clinical, and family studies, significantly expanding the resources available for dementia research. This work presents a replicable and governance-first model for the responsible management of complex, high-volume linked data. Looking ahead, we plan to migrate key computational modules, particularly the PRS generation and quality control workflows, to Nextflow. This migration will enhance computational reproducibility, enable portable and parallelized execution across high-performance environments, and further standardize analytical processes, directly advancing the methodological innovation and scalability goals of population data science.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3709"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426568/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing temperature exposure with time to birth using Bayesian joint models at population-scale; computationally tractable methods for exploring temporal associations incorporating full-parameter uncertainty.","authors":"James Rafferty, Amir Baniasadi, Wally Abdeldayem, Samantha Turner, Amy Mizen, Lucy Griffiths, Rhiannon Owen","doi":"10.23889/ijpds.v11i5.3662","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3662","url":null,"abstract":"<p><p>Climate change is affecting our world, and the impact of rising temperatures on health is not well understood. Prior work found exposure to heat was associated with reduced gestational age, increased prematurity and smaller birth weights. The goal of the Maternal and Pregnancy Health and Elevated Heat (MAGENTA) project is to determine if these patterns exist in a UK population. Utilising healthcare and environmental exposure data held in the Secure Anonymised Information Linkage (SAIL) Databank we developed a cohort of mothers who were pregnant between 2010 and 2023, and the associated daily maximum temperatures experienced during each pregnancy. Modelled Land Surface Temperature accounts for the effects of the built environment. The primary outcome was time to birth. We used a joint longitudinal and time-to-event model, constructed in a Bayesian framework to capture full parameter uncertainty and fit using the Integrated Nested Laplace Approximation (INLA). The longitudinal process modelled temperature experienced during the pregnancy with linear and quadratic terms for time. Time to birth was modelled using a Cox regression model with a spline baseline hazard, and smoothed at second order. Joint modelling is a flexible, powerful set of tools for understanding associations in healthcare research, and approximation methods such as INLA enable analysis in large-scale electronic health record datasets. Work is ongoing to produce fully adjusted models, which will be used in simulation studies in conjunction with climate change projections to explore the impact of future scenarios to inform mitigation and adaptation strategies.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3662"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pilot Samples for Estimation of Linkage Error.","authors":"Gavin Thomson, Matt Wray","doi":"10.23889/ijpds.v11i5.3629","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3629","url":null,"abstract":"<p><p>Efficient estimation of linkage error in large linked datasets requires careful allocation of limited clerical review resources. We investigate the use of small stratified \"pilot\" samples as an initial, low-cost strategy for informing sample size calculation and allocation across strata when estimating binomial error proportions. Stratified candidate links and unlinked pairs exhibit heterogeneous error rates and variances; however, without prior variance information, optimal allocation - such as Neyman allocation - cannot be applied. We show that pilot samples of approximately nh = 30 per stratum provide sufficiently informative variance estimates at minimal cost, enabling efficient allocation of subsequent sampling effort. Using binomial absolute margin-of-error behaviour as a rough guide we calculate uncertainty for error proportions up to a maximum of p = 0.5, and use simulations to show that beyond nh ≈ 30 the marginal reduction in uncertainty grows negligible. Simulations illustrate also that these small pilots successfully differentiate high- and low-variance strata, supporting targeted sampling in the final round. When pilots are combined with Neyman allocation, the resulting stratum-level confidence intervals were shown to become more homogeneous, reducing overall population-level variance relative to proportional allocation. Empirical comparisons therefore show that, for equal total clerical review effort, Neyman allocation informed by pilots consistently yields narrower population-level confidence intervals than proportional allocation (0.85% vs 0.93% MoE). This confirms that even very small pilot samples can materially improve efficiency by enabling variance-driven sample size calculation and allocation. The method offers a practical, scalable approach for organisations conducting clerical review of linkage errors in large datasets.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3629"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of EMR data-based LLM-based Disease Identification Framework to improve ICD data-based risk adjustment algorithms.","authors":"Seungwon Lee, Elliot Martin, Kiarash Riazi, Noopur Swadas, Cathy Eastwood, Jinli Yao, Robin Walker, Danielle Southern, Bing Li, Jeffrey Bakal","doi":"10.23889/ijpds.v11i5.3481","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3481","url":null,"abstract":"<p><p>Risk adjustment is essential for comparisons across populations by integrating individual health status and demographic factors to evaluate healthcare outcomes. We hypothesized that risk adjustment based on Large Language Models (LLMs) for case definitions, using electronic medical records (EMR) data, would outperform international classification of diseases (ICD) based algorithms in predicting inpatient mortality. Study Design and Methods A retrospective chart review cohort (n=10,659) consisting of randomly selected patients aged 18 years or older who were discharged from acute care settings was used. The chart review data were deterministically linked to an ICD database and an EMR database. We developed and applied an LLM-based framework (i.e., Phi-4) to EMR data for large-scale disease identification and compared it with ICD-based algorithms. To predict inpatient mortality, we calculated C-statistics using logistic regression. Among the cohort, 717 patients experienced inpatient mortality. Across all disease categories, LLM-based identification outperformed the ICD-data-based method. The ICD data-based risk adjustment algorithm achieved a C-statistic of 0.66 (95% CI 0.64 to 0.68) for in-hospital mortality, while the EMR data-based algorithm achieved a 0.76 (95% CI: 0.74 to 0.78) C-statistic. The chart review had a C-statistic of 0.71 (95% CI: 0.68 to 0.73) Effective risk adjustment for predicting health outcomes requires accurate information on patient comorbidity and demographic profiles. EMR data-based case definitions can account for disease severity (e.g., disease subtypes) and other variables (e.g., social determinants of health) that may not be readily available with historical ICD-based methods.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3481"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linking Geriatric Emergency Departments Using Deterministic and Machine Learning Methods.","authors":"Inessa Cohen, Cameron Gettel, Yuting Qian, Craig Rothenberg, Xi Chen, Ula Hwang","doi":"10.23889/ijpds.v11i5.3603","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3603","url":null,"abstract":"<p><strong>Objective: </strong>Evaluating geriatric emergency department (GED) interventions requires accurate longitudinal identification of GED sites and reliable linkage to hospital-level data, yet identifiers are often incomplete, inconsistent, or change over time. Our objective was to construct longitudinal GED identifiers by reconciling publicly available GED accreditation lists in the United States (US) linked to hospital characteristics in the American Hospital Association (AHA) survey.</p><p><strong>Approach: </strong>GED accreditation lists were first affirmed as GEDs and newly accredited GEDs using a two-stage fuzzy matching approach blocked by state. Sites were initially matched using hospital name similarity (distance ≤ 0.15), followed by city-based matching for remaining unmatched sites (distance ≤ 0.10). GEDs were then linked 1:1 to the AHA survey by manually assigning the AHAID using name and city. Discrepancies were resolved through multi-reviewer adjudication informed by geographic context, producing a curated reference linkage set of GEDs. In parallel, an XGBoost classifier paired GED sites with AHA hospitals in the same state using similarity features (name, city, teaching, rurality, and year difference) to independently reproduce curated linkages.</p><p><strong>Results: </strong>Among 545 US GEDs between 2018-2025, 482 had AHAIDs and 63 were missing. XGBoost reproduced 90% (95% CI: 87.8-92.5%) of curated linkages, with name similarity and city agreement accounting for over 85% of model importance by gain. Common challenges included name changes, system-level identifiers spanning multiple campuses, and accreditation turnover (sites gaining or losing accreditation).</p><p><strong>Conclusions: </strong>Accurate GED-AHA linkage is achievable, though sensitive to identifier instability, naming variation, and longitudinal changes.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3603"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linking administrative benefits and child protection data to uncover poverty's hidden impact.","authors":"Natalia Valdebenito Contreras, Tamara Godoy Jalil, Rick Hood","doi":"10.23889/ijpds.v11i5.3590","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3590","url":null,"abstract":"<p><p>This study examines the relationship between household financial circumstances and children's social care (CSC) involvement using newly linked local administrative data. Household benefits data (SHBE and UCDS) were securely linked and anonymised with Children in Need (CIN) records from six local authorities in London and South England, covering 2019-2021. Financial precarity is defined as households living below the relative poverty line or experiencing a cash shortfall. Children living in financial precarity were not more likely to be initially referred to CSC. However, once referred, they were significantly more likely to experience higher-intensity statutory interventions, including having a Child Protection Plan (12% vs 9%) and being re-referred (32% vs 29%). We estimate that an additional 270 Child Protection Plans were made over the study period among children referred from households below the poverty line. The 2020-21 Universal Credit (UC) uplift provides a natural experiment to examine the role of income support. Households receiving the uplift were substantially less likely to be in financial precarity, with a 17.5 percentage-point relative improvement. Children in uplift-eligible households were more likely to be referred to CSC but less likely to receive subsequent statutory protective interventions, suggesting that improved financial stability reduces the need for high-intensity CSC involvement. These findings demonstrate the value of ethically governed administrative data linkage for evaluating social policy. They highlight the importance of policies that strengthen families' financial circumstances as part of a preventative approach to improving child wellbeing and reducing statutory demand.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3590"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426605/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perinatal Opioid Use: Improving Insights through Federated Analysis of Linked Administrative Data.","authors":"Andi Camden, Hilary K Brown, Roxana Dragan, Tara Gomes, Jennifer A Hutcheon, Lauren Kelly, Hong Lu, Amy Metcalfe, Nazeem Muhajarine, Nathan Nickel","doi":"10.23889/ijpds.v11i5.3535","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3535","url":null,"abstract":"<p><strong>Objectives: </strong>Linked administrative data from 5 Canadian provinces were harmonized for the Canadian Perinatal Opioid Project, a nationally funded project. Using these data, we estimated opioid prescribing patterns during/after pregnancy at the national level and conducted cross-provincial comparisons.</p><p><strong>Approach: </strong>This population-based cohort study used linked administrative health records from Alberta, British Columbia, Manitoba, Ontario, and Saskatchewan. Included were all pregnancies to individuals aged 12-50 years, 2013-2023. Opioid use and prescribing patterns (type, duration, dose, timing) were measured from conception through to 1-year after the end of pregnancy. We conducted federated and province-specific analyses to produce pan-Canadian estimates of perinatal opioid use. Poisson models analyzed trends in perinatal opioid use and generated adjusted relative risks (aRR) of perinatal opioid use by socio-demographic and clinical factors. This research is being conducted in collaboration with people with lived/living experience.</p><p><strong>Results: </strong>Data from Alberta Health Services/Alberta Health, PopDataBC, Manitoba Centre for Health Policy, ICES, and Health Research Data Platform-Saskatchewan were harmonized. Federated data analyses are underway and will be completed by May 2026. We anticipate  3.2 million pregnancies (325,000 pregnancies/year). Preliminary analyses from Ontario show the prevalence of prenatal opioid exposure is 4.1% based on 1.6 million pregnancies.</p><p><strong>Conclusions/implications: </strong>Improving health system data access through multi-regional research using harmonized administrative data addresses important knowledge gaps and will generate the first pan-Canadian estimates of perinatal opioid use. This information is needed to inform prevention efforts, harm reduction, and tailored interventions for people who use opioids during/after pregnancy.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3535"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}