Clinical information prompt-driven retinal fundus image for brain health evaluation by Nuo Tong, Ying Hui, Shui-Ping Gou, Ling-Xi Chen, Xiang-Hong Wang, Shuo-Hua Chen, Jing Li, Xiao-Shuai Li, Yun-Tao Wu, Shou-Ling Wu, Zhen-Chang Wang, Jing Sun, Han Lv

“…The average difference between predicted and actual brain volumes was 61.36 cm3, with a relative error of 4.54%. When all of the clinical information (including age and sex, daily habits, cardiovascular factors, metabolic factors, and inflammatory factors) was encoded, the difference was decreased to 53.89 cm3, with a relative error of 3.98%. …”
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Local agricultural markets and external shocks – the case of Poland by Ciżkowicz Piotr, Janecki Jaroslaw, Olipra Jakub, Wojciechowski Wiktor

“…We carried out estimations using error correction and local projection econometric models. …”
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Frequency Stability Analysis Based on Full State Model in Autonomous-Synchronization Voltage Source Interfaced Power System by Zhenyao LI, Deqiang GAN, Moude LUAN, Guoqing HE

“…Finally, the correctness and effectiveness of the above conclusions and methods are verified by a system with 10 machines and 39 nodes.…”
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Deep learning-based anterior segment identification and parameter assessment of primary angle closure disease in ultrasound biomicroscopy images by Xingzhi Sun, Guotong Xie, Kun Lv, Xiaoyue Zhang, Bin Lv, Yuan Ni, Yao Ma, Fangting Li, Kangyi Yang, Jiayin Qin, Huijuan Wu

“…Our model got a mean IoU of 0.98, 0.98 and 0.99 on cornea segmentation, iris segmentation and ciliary body segmentation and a mean error distance of 0.49 pixels on scleral spur localisation in open-angle images and received 0.98, 0.98, 0.978 and 1.42 pixels respectively in angle-closure images. …”
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Antimicrobial Susceptibility Testing of the Combination of Aztreonam and Avibactam in NDM-Producing <i>Enterobacterales</i>: A Comparative Evaluation Using the CLSI and EUCAST Meth... by Linda Mei-Wah Chan, Doris Yui Ling Lok, River Chun Wai Wong, Alfred Lok-Hang Lee, Ingrid Yu-Ying Cheung, Christopher Koon-Chi Lai, Viola C. Y. Chow

“…<b>Results</b>: Using BDE as the standard of comparison, the AZA DD, AZA MTS, and SS methods had 100% categorical agreement (CA), 0% very major error (VME), and 0% major error (ME). The essential agreement (EA) between the AZA MTS and SS method was 57.5%. …”
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Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data by Cristian Constantin Volovăț, Călin Gheorghe Buzea, Diana-Ioana Boboc, Mădălina-Raluca Ostafe, Maricel Agop, Lăcrămioara Ochiuz, Ștefan Lucian Burlea, Dragoș Ioan Rusu, Laurențiu Bujor, Dragoș Teodor Iancu, Simona Ruxandra Volovăț

“…Models were trained using mean squared error loss and evaluated through stratified cross-validation and an independent held-out test set. …”
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AI‐assisted warfarin dose optimisation with CURATE.AI for clinical impact: Retrospective data analysis by Tiffany Rui Xuan Gan, Lester W. J. Tan, Mathias Egermark, Anh T. L. Truong, Kirthika Kumar, Shi‐Bei Tan, Sarah Tang, Agata Blasiak, Boon Cher Goh, Kee Yuan Ngiam, Dean Ho

“…CURATE.AI's predictive performance was then evaluated with a set of metrics that assessed both technical performance and clinical relevance. Results and conclusions In this retrospective study of 127 patients, CURATE.AI fared better in terms of Percentage Absolute Prediction Error and Percentage Prediction Error of 20% compared to other models in the literature. …”
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The effect of training data size on real-time respiration prediction using long short-term memory model by Wenzheng Sun, Jun Dang, Lei Zhang, Qichun Wei, Chao Li, Ye Liu, Huang Jing, Kanghua Huang, Yuanpeng Zhang, Bing Li

“…The prediction accuracy was stable when the TDS exceeded 90 s. Conclusions TDS selection could influence the respiration signal prediction accuracy of the LSTM model. …”
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The Western Ontario Meniscal Evaluation Tool Translated Into Italian Is a Reliable, Precise, and Responsive Patient-Reported Outcome Measure for Arthroscopic Meniscal Surgery by Michelangelo Palco, M.D., Gabriele Giuca, M.D., Giorgio Gasparini, M.D., Roberto Simonetta, M.D., Danilo Leonetti, M.D., Filippo Familiari, M.D.

“…Test-retest reliability was assessed using the intraclass correlation coefficient, and measurement precision was evaluated by calculating the standard error of measurement and the minimal detectable change. …”
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Response Monitoring Theta-Band Activities Across Emotional Contexts in Schizophrenia and Bipolar Spectrum Disorders by Takakuni Suzuki, Margo W. Menkes, Melvin G. McInnis, Jian Kang, Tara A. Niendam, Maureen A. Walton, Patricia J. Deldin, Ivy F. Tso, Stephan F. Taylor

“…Electroencephalogram (EEG) indicators of response monitoring, including error-related negativity (ERN) and theta-band activities (4–8 Hz), have been proposed as transdiagnostic indicators of cognitive control. …”
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The Current Status of Microscopical Hair Comparisons by Walter F. Rowe

“…A review of the available scientific literature on microscopical hair comparisons (including studies conducted by the Royal Canadian Mounted Police and the Federal Bureau of Investigation) leads to three conclusions: (1) microscopical comparisons of human hairs can yield scientifically defensible conclusions that can contribute to criminal investigations and criminal prosecutions, (2) the reliability of microscopical hair comparisons is strongly affected by the training of the forensic hair examiner, (3) forensic hair examiners cannot offer estimates of the probability of a match of a questioned hair with a hair from a randomly selected person. …”
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How well do mothers recall their own and their infants’ perinatal events? A two-district study using cross-sectional stratified random sampling in Bihar, India by Caroline Jeffery, Joseph James Valadez, Baburam Devkota, Wilbur C Hadden

“…Objective Global monitoring of maternal, newborn and child health (MNCH) programmes use self-reported data subject to recall error which may lead to incorrect decisions for improving health services and wasted resources. …”
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Improved aboveground biomass estimation and regional assessment with aerial lidar in California’s subalpine forests by Sara Winsemius, Chad Babcock, Van R. Kane, Kat J. Bormann, Hugh D. Safford, Yufang Jin

“…When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. …”
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THE SUMMER TEMPERATURES FORECAST BY THE METHOD OF SINGULAR-SPECTRAL ANALYSIS IN THE SOUTH OF RUSSIA IN 2019-2023 by A. A. Tashilova, B. A. Ashabokov, L. A. Kesheva, N. V. Teunova

“…Conclusions: According to the forecast for the period 2019-2023 average summer tem- perature tends to further increase. …”
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Multiscale End-point Screening with Extended Tight-binding Hamiltonians by Xiaohui Wang, Sai Li, Zuoyuan Zhang, Linqiong Qiu, Zhaoxi Sun

“…Notably, in challenging systems like sulfur-substituted pillararenes, xTB methods exhibited superior performance, whereas MM/GBSA failed due to inadequate error cancellation. The use of CPCM-X did not further enhance accuracy, possibly due to unsuccessful error cancellation. …”
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Identification of intragenic variants in pediatric patients with intellectual disability in Peru by Hugo Hernán Abarca-Barriga, Flor Vásquez Sotomayor, Renzo Punil-Luciano, María Cristina Laso-Salazar, Heli Jaime Barrón-Pastor

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Automated Extraction of Stroke Severity From Unstructured Electronic Health Records Using Natural Language Processing by Marta Fernandes, M. Brandon Westover, Aneesh B. Singhal, Sahar F. Zafar

“…Conclusions The automatic natural language processing–based model can enable large‐scale stroke severity phenotyping from the electronic health record and support real‐world quality improvement and comparative effectiveness studies in stroke.…”
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Estimation of Static Lung Volumes and Capacities From Spirometry Using Machine Learning: Algorithm Development and Validation by Scott A Helgeson, Zachary S Quicksall, Patrick W Johnson, Kaiser G Lim, Rickey E Carter, Augustine S Lee

“…The classification models showed a robust performance overall, with relatively low root mean square error and mean absolute error values observed across all predicted lung volumes. …”
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Impact of implant scan body material and angulation on the trueness and precision of digital implant impressions using four intraoral scanners–an in vitro study by Hisham Soltan, Xiaoxue Mai, Amr S. Ramdan, Mohammed Qasem Saleh, Sarraj. H. Ashour, Weibo Xie

“…A high-resolution desktop scanner provided the reference. Trueness (RMS error vs. reference model) and precision (RMS error from intra-group comparisons) were calculated using Geomagic software. …”
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Can Emotional and Behavioral Dysregulation in Youth Be Decoded from Functional Neuroimaging? by Liana C L Portugal, Maria João Rosa, Anil Rao, Genna Bebko, Michele A Bertocci, Amanda K Hinze, Lisa Bonar, Jorge R C Almeida, Susan B Perlman, Amelia Versace, Claudiu Schirda, Michael Travis, Mary Kay Gill, Christine Demeter, Vaibhav A Diwadkar, Gary Ciuffetelli, Eric Rodriguez, Erika E Forbes, Jeffrey L Sunshine, Scott K Holland, Robert A Kowatch, Boris Birmaher, David Axelson, Sarah M Horwitz, Eugene L Arnold, Mary A Fristad, Eric A Youngstrom, Robert L Findling, Mirtes Pereira, Leticia Oliveira, Mary L Phillips, Janaina Mourao-Miranda

“…The correlation and the mean squared error between actual and decoded symptoms were significant at the initial study screen and close to the fMRI scanning session. …”
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