Background: Limited knowledge about the prediction accuracy of exposure models hinders the interpretation of results from epidemiological studies on childhood cancer risks associated with exposure to background gamma-radiation.
Objective: We aimed to validate a spatial exposure model that we recently developed for Switzerland.
Methods: We used individual exposure measurements conducted with D-Shuttle dosimeters by 149 children throughout the country. We ran linear regression models fitting the measured exposure against predictions from the newly developed model, and compared results with the predictions from an earlier model. We further used variograms to investigate the spatial correlation of estimation errors.
Results: The prediction accuracy of the newly developed exposure model was modest (R = 0.2), but better than the earlier model (R = 0.13). Prediction errors revealed weak spatial correlation.
Discussion: Although the new exposure model marks an improvement, the modest prediction accuracy and the remaining spatial correlation of errors show room for further improvement. Our study highlights the need for validation of exposure models for background gamma-radiation used in epidemiological studies.
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http://dx.doi.org/10.1016/j.jenvrad.2024.107581 | DOI Listing |
Diagn Progn Res
January 2025
Department of Applied Health Sciences, College of Medicine and Health, University of Birmingham, Edgbaston, Birmingham, UK.
Background: Pressure injuries (PIs) place a substantial burden on healthcare systems worldwide. Risk stratification of those who are at risk of developing PIs allows preventive interventions to be focused on patients who are at the highest risk. The considerable number of risk assessment scales and prediction models available underscores the need for a thorough evaluation of their development, validation, and clinical utility.
View Article and Find Full Text PDFKnee Surg Relat Res
January 2025
Bioengineering Laboratory, Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
Background: Unplanned readmission, a measure of surgical quality, occurs after 4.8% of primary total knee arthroplasties (TKA). Although the prediction of individualized readmission risk may inform appropriate preoperative interventions, current predictive models, such as the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator (SRC), have limited utility.
View Article and Find Full Text PDFBMC Res Notes
January 2025
Department of Computer Engineering, Chungbuk National University, Chungdae-ro 1, Cheongju, 28644, Republic of Korea.
Background: Drug response prediction can infer the relationship between an individual's genetic profile and a drug, which can be used to determine the choice of treatment for an individual patient. Prediction of drug response is recently being performed using machine learning technology. However, high-throughput sequencing data produces thousands of features per patient.
View Article and Find Full Text PDFInt J Retina Vitreous
January 2025
Department of Retina and Vitreous, Narayana Nethralaya, #121/C, 1st R Block, Chord Road, Rajaji Nagar, Bengaluru, 560010, India.
Purpose: To evaluate the predictive accuracy of various machine learning (ML) statistical models in forecasting postoperative visual acuity (VA) outcomes following macular hole (MH) surgery using preoperative optical coherence tomography (OCT) parameters.
Methods: This retrospective study included 158 eyes (151 patients) with full-thickness MHs treated between 2017 and 2023 by the same surgeon and using the same intraoperative surgical technique. Data from electronic medical records and OCT scans were extracted, with OCT-derived qualitative and quantitative MH characteristics recorded.
Biomark Res
January 2025
Department of Liver Surgery and Transplantation, Liver Cancer Institute and Zhongshan Hospital, Fudan University180 Fenglin Road, Shanghai, 200032, China.
Background: Predicting the efficacy of immune-based therapy in patients with unresectable hepatocellular carcinoma (HCC) remains a clinical challenge. This study aims to evaluate the prognostic value of the systemic immune-inflammation index (SII) in forecasting treatment response and survival outcomes for HCC patients undergoing immune-based therapy.
Methods: We analyzed a cohort of 268 HCC patients treated with immune-based therapy from January 2019 to March 2023.
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