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  1. 2821

    Association between different hemoglobin glycation index and prognosis in patients with a first diagnosis of acute myocardial infarction: a retrospective study based on the MIMIC-I... by Dong Chen, Dong Chen, Ben Hu, Xing-hua Chen, Xing Wei, Jun Feng, Ze-ping Hu

    Published 2025-05-01
    “…BackgroundThe hemoglobin glycation index (HGI) is defined as the difference between the observed and predicted values of glycosylated hemoglobin (HbA1c), which is closely associated with a variety of poor prognoses. …”
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  2. 2822

    Quantitative genetic analysis reveals potential to genetically improve fruit yield through selection in Solanum lycopersicum × Solanum pimpinellifolium crosses under humid conditio... by Chikezie Onuora Ene, Wosene Gebreselassie Abtew, Happiness Ogba Oselebe, Temesgen Matiwos Menamo, Friday Ugadu Ozi, Chibueze Kelechi Ene, Nnamdi Ogwo

    Published 2025-06-01
    “…The genetic effects were estimated using the Hayman model using the best linear unbiased prediction values. Significant differences in traits were observed among parental lines and their crosses. …”
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  3. 2823

    Risk factors and risk‐indicating model for early‐onset neonatal sepsis after preterm prelabor rupture of membranes: A historical cohort study by Brynhildur Tinna Birgisdottir, Tomas Andersson, Ingela Hulthén Varli, Sissel Saltvedt, Farhad Abtahi, Ulrika Åden, Malin Holzmann

    Published 2025-08-01
    “…We combined variables associated with high risk into a risk‐indicating model and estimated its performance by calculating its sensitivity, specificity, accuracy, positive and negative predictive values, positive likelihood ratio, and area under the ROC curve. …”
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  4. 2824

    DYNAMICS OF INCIDENCE WITH MYOCARDIAL INFARCTION IN THE POPULATION OF KUZBASS AND THE RUSSIAN FEDERATION FOR THE PERIOD 2011-2019 by Андрей Георгиевич Петров, Николай Владимирович Абрамов, Сергей Анатольевич Макаров, Сергей Николаевич Филимонов, Виктор Андреевич Семенихин, Наталья Степановна Черных

    Published 2022-03-01
    “…In order to establish a trend and identify the dynamics of the studied number of indicators as it would be outside the influence of «random» factors, the method of alignment by the method of least squares was applied. The predicted values of the incidence of myocardial infarction in Kuzbass and the Russian Federation have been calculated. …”
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  5. 2825

    Validation of an Automated High-Throughput Multiplex Real-Time PCR Assay for Detection of Enteric Protozoa by Rachel Lau, Jason Kwan, Kimberley Marks-Beaubrun, Ruben Cudiamat, Min Qun Ellen Chen, Krista Orejana, Filip Ralevski, Andrea K. Boggild

    Published 2025-03-01
    “…<b>Results</b>: Among 461 unpreserved fecal specimens, sensitivity, specificity, positive predictive and negative predictive values of the enteric multiplex for fresh specimens were as follows: 93%, 98.3%, 85.1%, 99.3% for Bh; 100% for all measures in <i>Cryptosporidium</i> and Cc; 100%, 99.3%, 88.5%, 100% for Df; 33.3%, 100%, 100%, 99.6% for Eh; and 100%, 98.9%, 68.8%, 100% for Gl, respectively. …”
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  6. 2826

    Evaluation of Noninvasive Adjuncts for Early Detection of Oral Cancer in Oral Potentially Malignant Disorders and Development of Risk-Based Management Strategies: Protocol for a Pr... by Ruchika Gupta, Apurva Ratnu, Shalini R Gupta, Hariprakash Hadial, Lucky Singh, Prashant Kumar Singh, Shalini Singh

    Published 2025-05-01
    “…Sensitivity, specificity, and negative and positive predictive values of these adjunctive techniques (autofluorescence and TB) in the detection of oral cancer shall be calculated using biopsy as the gold standard. …”
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  7. 2827

    Improving prediction accuracy of hospital arrival vital signs using a multi-output machine learning model: a retrospective study of JSAS-registry data by Yasuyuki Kawai, Koji Yamamoto, Keisuke Tsuruta, Keita Miyazaki, Hideki Asai, Hidetada Fukushima

    Published 2025-05-01
    “…Model performance was assessed by comparing the predicted values with the actual hospital arrival measurements using mean absolute error, R² score, residual standard deviation, and Spearman’s correlation coefficient. …”
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  8. 2828

    Development and validation of a prediction model for the risk of citrate accumulation in critically ill patients with citrate anticoagulation for continuous renal replacement thera... by Zhi-Qing Hu, Zheng-Long Ye, Hui Zou, Shang-Xiang Liu, Cheng-Qing Mei

    Published 2025-04-01
    “…The calibration curves in the training set and internal/external validation sets showed a high degree of consistency between predicted values and observed values (according to the Hosmer-Lemeshow test, the P-values were 0.7673, 0.2401, and 0.4512 for the training set, internal validation set, and external validation set, respectively). …”
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  9. 2829

    Projected changes and burden of type 2 diabetes in China from 1990 to 2021: an analysis of GBD 2021 data by Junhao MA, Guangqiang MIAO, Guixiao WANG, Shen LI, Liping YANG

    Published 2025-05-01
    “…The dynamic trends of the predicted values of the ARIMA model, NNAR model, and combined ARIMA-NNAR model were basically consistent with the actual situation, but the values of each evaluation index of the combined prediction model showed that the prediction effect was better than that of the single prediction model. …”
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  10. 2830

    Multicenter development of a deep learning radiomics and dosiomics nomogram to predict radiation pneumonia risk in non-small cell lung cancer by Xun Wang, Aiping Zhang, Huipeng Yang, Guqing Zhang, Junli Ma, Shucheng Ye, Shuang Ge

    Published 2025-05-01
    “…The calibration curve showed that the predicted value of DLRDN was in good agreement with the actual value. …”
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  11. 2831

    Investigation of the Effects of Different Plyometric Training Protocols on Punching Force and Muscle Performance in Male Boxers by Mehmet Söyler, Alper Cenk Gürkan, İdris Kayantaş, Serhat Aydın, Baykal Karataş, Meriç Eraslan, Musa Şahin, Hamza Küçük, Adela Badau, Dana Badau

    Published 2025-06-01
    “…Adjusting for the strength-to-weight ratio significantly influenced the predictive values in intergroup comparisons (<i>p</i> < 0.005). …”
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  12. 2832

    Analysis of factors affecting the accuracy of 18F-fluorodeoxyglucose positron emission tomography combined with computed tomography in case of suspected prosthetic valve infective... by I. P. Aslanidi, E. Z. Golukhova, D. M. Pursanova, O. V. Mukhortova, I. V. Shurupova, I. V. Ekaeva, T. A. Katunina, T. A. Trifonova

    Published 2022-01-01
    “…Thus, the sensitivity, specificity and diagnostic accuracy of PET/CT in the diagnosis of PVE were 92%, 67% and 84%, respectively; positive and negative predictive values — 85% and 80%. The analysis of the odds ratio did not reveal the relationship of low inflammatory activity, the interval between surgery and PET/CT from 3 to 6 months, and long-term ABT before PET/CT with false PET/CT results (p&gt;0,05). …”
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  13. 2833

    Evaluation of the GeneXpert MTB/RIF in patients with presumptive tuberculous meningitis. by Tatiana Metcalf, Jaime Soria, Silvia M Montano, Eduardo Ticona, Carlton A Evans, Luz Huaroto, Matthew Kasper, Eric S Ramos, Nicanor Mori, Podjanee Jittamala, Kesinee Chotivanich, Irwin F Chavez, Pratap Singhasivanon, Sasithon Pukrittayakamee, Joseph R Zunt

    Published 2018-01-01
    “…Considering the 30 patients who had a clinical diagnosis of TBM: diagnostic sensitivity was 23% (7/30, 95%CI: 9.9-42%) for GeneXpert and was the same for all culture results combined; considerably greater than 7% (2/30, 95%CI: 0.82-22%) for microscopy; whereas all laboratory tests had poor negative predictive values (20-23%). Considering only the 8 patients with definite TBM: diagnostic sensitivity was 88% (7/8, 95%CI: 47-100%) for GeneXpert; 75% (6/8, 95%CI: 35-97%) for MGIT culture or LJ culture; 50% (4/8, 95%CI 16-84) for Ogawa culture and 25% (2/8, 95%CI: 3.2-65%) for microscopy. …”
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  14. 2834

    Improving chest pain risk assessment: validation of HEART, TIMI, GRACE, EDACS-ADP, and HET for MACE prediction in the emergency department by Mehdi Nasr Isfahani, Hamidreza Mohseni, Elahe Nasri Nasrabadi, Nizal Sarrafzadegan

    Published 2025-08-01
    “…Diagnostic performance of the risk scores was evaluated using receiver operating characteristic (ROC) curve analysis, including calculation of sensitivity, specificity, positive and negative predictive values, and likelihood ratios at clinically relevant cut-off thresholds. …”
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  15. 2835

    Clinical utility of metagenomic next-generation sequencing in pathogen detection for lower respiratory tract infections by Lan Min Lai, Qian-bing Dai, Mei Ling Cao, Yang Liu, Rui Zhao, Lei Yuan

    Published 2025-05-01
    “…From these cases, mNGS identified 76.8% (307/400) truepositive cases, 8.0% (32/400) falsepositive cases, 9.8% (39/400) truenegative cases, and 5.5% (22/400) falsenegative cases. mNGS demonstrated significantly higher sensitivity [93.3% (307/329) vs. 55.6(183/329)%] alongside greater negative predictive values [63.9% (39/61) vs.25.9%(51/197)], whereas culture offered higher specificity [54.9%(39/71) vs. 71.8%(51/71)]. …”
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  16. 2836

    Evaluation of predictive performance of modeling hyperuricemia using medical big data: comparison of data preprocessing methods by Luwei Li, Xian Huang, Cijin Yan, Shuzhan He, Sishuai Cheng, WenJie Yang

    Published 2025-04-01
    “…Then, the continuous variables in the raw data were assigned values to become categorical variables, and statistical analysis was performed using the same algorithm to obtain the predicted values of the two models. ROC curve analysis, Calibration curve analysis, DCA curve analysis, and CIC curve analysis were performed to comprehensively evaluate the accuracy, discriminatory ability, and clinical practicality of the two models. …”
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  17. 2837

    Building a risk prediction model for anastomotic leakage postoperative low rectal cancer based on Lasso-Logistic regression by Zhenhao Quan, Lin Lin, Renwei Huang, Kaiyu Sun, Feipeng Xu

    Published 2025-07-01
    “…H-L goodness of fit test showed that there was no significant difference between the predicted value of the model and the actual observed value (χ 2  = 6.438, P = 0.598). …”
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  18. 2838

    Cerebrospinal Fluid Leakage Combined with Blood Biomarkers Predicts Poor Wound Healing After Posterior Lumbar Spinal Fusion: A Machine Learning Analysis by Pang Z, Ou Y, Liang J, Huang S, Chen J, Huang S, Wei Q, Liu Y, Qin H, Chen Y

    Published 2024-11-01
    “…Calibration curve analysis showed good consistency between nomogram-predicted values and actual measurements.Conclusion: SLSI, albumin, postoperative glucose, CSFL, NEU and CRP were identified as significant risk factors for PWH after posterior lumbar spinal fusion. …”
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  19. 2839

    Identifying mating events of group-housed broiler breeders via bio-inspired deep learning models by Venkat U.C. Bodempudi, Guoming Li, J. Hunter Mason, Jeanna L. Wilson, Tianming Liu, Khaled M. Rasheed

    Published 2025-07-01
    “…With custom training, the best performance of detecting broiler breeders via YOLOv8l was over 0.939 precision, recall, mAP50, mAP95, and F1 score for training and 0.95 positive and negative predicted values for testing. After comparing 24 scenarios of mating duration and 32 scenarios of time interval, a mating duration of 3-9 seconds and the time intervals of T-3 to T+12 seconds based on manual observation were incorporated into the framework to filter out unnecessary data and retain keyframes for further processing, significantly reducing the processing speed by a factor of 10. …”
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  20. 2840

    Sustainable extraction of phytochemicals from Mentha arvensis using supramolecular eutectic solvent via microwave Irradiation: Unveiling insights with CatBoost-Driven feature analy... by Zubera Naseem, Muhammad Bilal Qadir, Abdulaziz Bentalib, Zubair Khaliq, Muhammad Zahid, Fayyaz Ahmad, Nimra Nadeem, Anum Javaid

    Published 2025-04-01
    “…The lowest average relative errors of 0.402 % (TPC), 0.863 % (TFC), and 0.597 % (DPPH) for train and 0.679 % (TPC), 0.685 % (TFC) and 0.480 % (DPPH) for test data showed the consistency with the predicted values. The partial dependence and feature importance revealed the contributing impact of parameters for optimizing the extraction. …”
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