Performance of Xpert MTB/RIF by Using Gastric Lavage Sample in The Diagnosis of Pulmonary Tuberculosis in HIV Patients with Suspected Pulmonary Tuberculosis by Manuel Lamberto Willem Mboeik, Ceva Wicaksono Pitoyo, Teguh Harjono Karjadi, Anis Karuniawati, Esthika Dewiasty

“…Using clinical diagnosis of pulmonary TB as gold standard, a single Xpert MTB/RIF assay using gastric lavage sample showed sensitivity of 50.98% (95% CI: 36.6-65.25), specificity 87.88% (95% CI: 77,1-94,62), positive predictive value 76,47% (95%CI: 61,67-86,78) and negative predictive value 69,88% (95% CI: 63,36-75.68). …”
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Application of Thyroid Hormones in Women’s Hair for the Non-Invasive Prediction of Graves’ Disease by Kouhei Igarashi, Chie Takita, Masako Matsumoto, Wataru Kitagawa, Atsuko Ota, Naoko Miyazaki, Koichi Ito, Kazutaka Ikeda

“…The cut-off value was 0.133 pg/mg (sensitivity: 91.2%; specificity: 100%; positive predictive value (PPV): 100%; negative predictive value (NPV): 76.9%) for FT3 and 0.067 pg/mg (sensitivity: 70.6%; specificity: 100%; PPV: 100%; NPV: 50.0%) for FT4. …”
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The Cut-Off Value for the Triglyceride–Glucose Index (TyG) Discriminating Insulin Resistance Based on the SHBG Level and HOMA-IR Values in Women with Polycystic Ovary Syndrome by Tahar Ben Rhaiem, Aleksander Jerzy Owczarek, Mariusz Wójtowicz, Dariusz Ciura, Paweł Madej, Jerzy Chudek, Magdalena Olszanecka-Glinianowicz

“…The empirical optimal of the TyG index cut-off, corresponding to HOMA-IR ≥ 2.1, was >8.31 (AUC 0.77, accuracy 0.70, sensitivity 61.2%, specificity 75.3%, PPV—positive predictive value 59.4%, NPV—negative predictive value 76.7%). …”
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Contrast-enhanced CT-based deep learning model assists in preoperative risk classification of thymic epithelial tumors by Xuhui Zhao, Lingyu Zhang, Li Liang, Qi Zhang, Wencan Wang, Junlin Li, Hua Zhang, Chunhai Yu, Lingjie Wang

“…Diagnostic performance of junior and senior radiologists in distinguishing low-risk thymoma (LRT) from high-risk thymoma (HRT) was assessed with and without the assistance of the optimal DL model.ResultsThe ResNet 101 model emerged as the best performer among six DL models, achieving an AUC of 0.876, accuracy of 0.820, sensitivity of 0.878, specificity of 0.700, positive predictive value of 0.857, and negative predictive value of 0.737 in the external testing set, outperforming the traditional radiomic model (AUC, p < 0.05). …”
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Assessing the validity of Society of Thoracic Surgeons (STS) score in predicting stroke risk among patients undergoing cardiothoracic surgery at a tertiary hospital in Pakistan: a... by Javerya Hassan, Namra Usman, Muhammad Salman, Arshan Ali, Maryam Shaukat, Hana Khan, Ahmed Raheem, Hasanat Sharif

“…In this article, we aim to investigate the predictive value of Society of Thoracic Surgeons (STS) score’s effectiveness in stroke risk in cardiothoracic surgery patients in our population. …”
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Diagnostic performances of adding transition and hepatobiliary phase to washout in gadoxetic acid-enhanced MRI for subcentimeter hepatocellular carcinoma by Li Yang, Shengxiang Rao, Kaipu Jin, Lifang Wu, Mengsu Zeng, Ying Ding

“…Assessment Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy with 95% confidence interval were calculated for WO confined to the PVP and WO extended to the TP or HBP, and generalized estimating equation was used to compare sensitivity, specificity, PPV, NPV and accuracy. …”
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Analysis of the effect of fecal SDC2, ADHFE1 and PPP2R5C gene methylation test for screening colorectal cancer in the Otog Front Banner by Fan Zeng, Shiiu Chen, Xuli Zhu, Jinrun Chen, Maochong Lan, Runxiang Chen, Daya Zhang, Chen Chen, Shimei Huang, Da Li, Xiaodong Zhang, Feihu Bai

“…The chi-square test was utilized to analyze the positive rates of fecal SDC2, ADHFE1, and PPP2R5C gene methylation screenings, colonoscopy compliance, the positive predictive value of intestinal lesions, and to assess the risk factors associated with cancer. …”
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Correlation between serum magnesium level and arteriosclerosis in maintenance hemodialysis patients by Dan-yan Min, Ting Shen, Ting Zhang, Jun-jun Zhu, Miao Liu, Qiu-nan Zhan, Jun-li Zhao

“…And serum magnesium level has an excellent predictive value for the occurrence of arteriosclerosis.…”
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QUALITATIVE TEST FOR THE FATTY ACID BINDING CARDIAC PROTEIN IN EARLY DIAGNOSTICS OF MYOCARDIAL INFARCTION: SUBANALYSIS OF RUSSIAN MULTI-CENTER STUDY ISPOLIN by V. A. Kokorin, М. Z. Gasanov, I.  G. Gordeev, А. М. Gritsanchuk, N. А. Dzhaiani, R. М. Kalinichenko, V. V. Kashtalap, F. Yu. Kopylov, I. V. Kositsyna, Е. S. Kylbanova, Е. S. Lapina, Е. D. Laryushkina, V. N. Nikanorov, А. V. Khripun, S. S. Yakushin

“…For both markers, the values were assessed of sensitivity, specificity, diagnostic accuracy, positive and negative predictive value in general, and various timing intervals from the moment of clinical ACS signs presentation and ECG changes.Results. …”
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Development of a thrombin-antithrombin complex detection kit and study in venous thromboembolism complicated by cervical cancer by Yanru Qiu, Shuang Han, Yu Ji, Zhixian Lu, Xuan Huang

“…Elevated TAT levels have significant predictive value for thrombosis occurrence in cervical cancer patients. …”
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Screening the Global Health Priority Box against Plasmodium berghei liver stage parasites using an inexpensive luciferase detection protocol by Gia-Bao Nguyen, Caitlin A. Cooper, Olivia McWhorter, Ritu Sharma, Anne Elliot, Anthony Ruberto, Rafael Freitas, Ashutosh K. Pathak, Dennis E. Kyle, Steven P. Maher

“…Retrospective analyses show excellent predictive value for both anti-malarial activity and cytotoxicity. …”
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Machine learning-based radiomics for differentiating lung cancer subtypes in brain metastases using CE-T1WI by Xueming Xia, Wei Du, Qiheng Gou

“…The models’ performance was evaluated using receiver operating characteristic (ROC) analysis and area under the curve (AUC) calculations, complemented by additional metrics such as accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV).ResultsA total of 833 radiomic features were extracted from the ROIs. …”
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Accuracy and usability of AcuPebble SA100 for automated diagnosis of obstructive sleep apnoea in the home environment setting: an evaluation study by Swapna Mandal, Nikesh Devani, Esther Rodriguez-Villegas, Renard Xaviero Adhi Pramono, Syed Anas Imtiaz, Stuart Bowyer

“…Comparing against the current American Academy of Sleep Medicine’s AHI-based criteria demonstrated 95.33% accuracy (95% CI (90·62% to 98·10%)), 96.84% specificity (95% CI (91·05% to 99·34%)), 92.73% sensitivity (95% CI (82·41% to 97·98%)), 94.4% positive-predictive value (95% CI (84·78% to 98·11%)) and 95.83% negative-predictive value (95% CI (89·94% to 98·34%)). …”
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GRACE score in assessing the risk of hospital outcomes in patients with pulmonary embolism by I. S. Mullova, N. A. Cherepanova, T. V. Pavlova, S. M. Khokhlunov, D. A. Gnilomedova, A. A. Leksina, D. V. Duplyakov

“…The GRACE score showed a high predictive value for adverse outcomes in PE patients with a sensitivity of 96% and a specificity of 63%. …”
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Comparison of Specific Immunoglobulin E with the Skin Prick Test in the Diagnosis of House Dust Mites and Cockroach Sensitization in Patients with Asthma and/or Allergic Rhinitis by Suriani Alimuddin, Iris Rengganis, Cleopas M Rumende, Siti Setiati

“…The diagnostic result includes sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+), and negative likelihood ratio (LR-). …”
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Platelet-derived growth factor receptor-β as a non-invasive biomarker for liver fibrosis prediction in Egyptian diabetic patients with metabolic-associated fatty liver disease by Hanaa Badran, Maha Elsabaawy, Mai Magdy, Hazem Omar, Olfat Hendy, Eman Awaad, Maymona Abd El-Wahed Al-Khalifa, Mai Abozeid

“…Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of PDGFRβ to predict significant liver fibrosis in diabetic MAFLD patients were 85%, 93.33%, 89.5%, and 90.3%, respectively, at a cutoff > 2.54, and were 85.71%, 51.52%, 27.3%, and 94.4% at a cutoff > 1.59 in nondiabetics. …”
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Targeting genomic receptors in voided urine for confirmation of benign prostatic hyperplasia by Mathew Thakur, Vivek S. Tomar, Emma Dale, Leonard G. Gomella, Charalambos Solomides, Oleksandr Kolesnikov, Scott W. Keith, Hector T. Navarro, Olivia Dahlgren, Michael Chaga, Edouard J. Trabulsi

“…Specificity for VPAC urine assay for excluding prostate cancer in this BPH cohort was 88.5%, positive predictive value 0.00% and negative predictive value 100%. …”
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Preliminary Evaluation of the Scandinavian Guidelines for Initial Management of Minimal, Mild, and Moderate Head Injuries with Glial Fibrillary Acidic Protein by Mira Keski-Pukkila, Justin E. Karr, Jussi P. Posti, Ksenia Berghem, Anna-Kerttu Kotilainen, Kaj Blennow, Henrik Zetterberg, Grant L. Iverson, Teemu M. Luoto

“…Within the Mild (Low Risk) group (n = 49), GFAP sensitivity (with 95% confidence intervals in parentheses) for detecting traumatic CT abnormalities was 1.0 (0.40–1.00), specificity 0.34 (0.19–0.53), the negative predictive value (NPV) 1.0 (0.68–1.00), and the positive predictive value (PPV) 0.16 (0.05–0.37). …”
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Examining the utility and accuracy of the interRAI Emergency Department Screener in identifying high‐risk older emergency department patients: A Canadian multiprovince prospective... by Fabrice I. Mowbray, George Heckman, John P. Hirdes, Andrew P. Costa, Olivier Beauchet, Debra Eagles, Jeffrey J. Perry, Samir Sinha, Patrick Archambault, Hanting Wang, Michaela Jantzi, Paul Hebert

“…We calculated the sensitivity, positive predictive value, and false discovery rate of the interRAI ED Screener. …”
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Comparative performance of Kato–Katz, POC-CCA and real-time PCR in detecting Schistosoma mansoni infection at different endemicity settings in northwest Ethiopia: a cross-sectional... by Getaneh Alemu, Endalkachew Nibret, Abaineh Munshea, Melaku Anegagrie, María Flores-Chávez, Tadesse Hailu, Arancha Amor

“…Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and kappa statistics were calculated using the Simple Interactive Statistical Analysis (SISA) online tool. …”
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