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  1. 74981
    “…RESULTS: Regarding the first aim, we observed variances in discriminative metrics and calibration errors of ML models when different baseline methods were adopted. …”
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    Machine learning clinical prediction models for acute kidney injury: the impact of baseline creatinine on prediction efficacy
  2. 74982
    “…When these two factors were added to other known risk factors in multivariable models, clinicopathological risk factors alone were found to be limited in discriminating between low and high risk DCIS.…”
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    Association of DCIS size and margin status with risk of developing breast cancer post-treatment: multinational, pooled cohort study
  3. 74983
    “…The model showed good discriminative ability (area under curve = 0.778), calibrating ability and clinical utility. …”
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    A multi-center cross-sectional study on identification of influencing factors of medical students’ emotional engagement in China
  4. 74984
    “…The ball-on-bar task showed the most discriminative ability for both bimanual coupling and interlimb differences, while the object-hit and circuit tasks are unique to interlimb differences and bimanual coupling, respectively. …”
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    In-depth quantification of bimanual coordination using the Kinarm exoskeleton robot in children with unilateral cerebral palsy
  5. 74985
    “…Finally, various models including different combinations of demographics, genetics, and Aβ plasma levels were constructed using logistic regression and area under the receiver operating characteristic curve (AUROC) analyses to evaluate their ability for discriminating which subjects presented brain amyloidosis. …”
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    Correlations between plasma and PET beta-amyloid levels in individuals with subjective cognitive decline: the Fundació ACE Healthy Brain Initiative (FACEHBI)
  6. 74986
    por Page, Elizabeth C., Bancroft, Elizabeth K., Brook, Mark N., Assel, Melissa, Hassan Al Battat, Mona, Thomas, Sarah, Taylor, Natalie, Chamberlain, Anthony, Pope, Jennifer, Raghallaigh, Holly Ni, Evans, D. Gareth, Rothwell, Jeanette, Maehle, Lovise, Grindedal, Eli Marie, James, Paul, Mascarenhas, Lyon, McKinley, Joanne, Side, Lucy, Thomas, Tessy, van Asperen, Christi, Vasen, Hans, Kiemeney, Lambertus A., Ringelberg, Janneke, Jensen, Thomas Dyrsø, Osther, Palle J.S., Helfand, Brian T., Genova, Elena, Oldenburg, Rogier A., Cybulski, Cezary, Wokolorczyk, Dominika, Ong, Kai-Ren, Huber, Camilla, Lam, Jimmy, Taylor, Louise, Salinas, Monica, Feliubadaló, Lidia, Oosterwijk, Jan C., van Zelst-Stams, Wendy, Cook, Jackie, Rosario, Derek J., Domchek, Susan, Powers, Jacquelyn, Buys, Saundra, O'Toole, Karen, Ausems, Margreet G.E.M., Schmutzler, Rita K., Rhiem, Kerstin, Izatt, Louise, Tripathi, Vishakha, Teixeira, Manuel R., Cardoso, Marta, Foulkes, William D., Aprikian, Armen, van Randeraad, Heleen, Davidson, Rosemarie, Longmuir, Mark, Ruijs, Mariëlle W.G., Helderman van den Enden, Apollonia T.J.M., Adank, Muriel, Williams, Rachel, Andrews, Lesley, Murphy, Declan G., Halliday, Dorothy, Walker, Lisa, Liljegren, Annelie, Carlsson, Stefan, Azzabi, Ashraf, Jobson, Irene, Morton, Catherine, Shackleton, Kylie, Snape, Katie, Hanson, Helen, Harris, Marion, Tischkowitz, Marc, Taylor, Amy, Kirk, Judy, Susman, Rachel, Chen-Shtoyerman, Rakefet, Spigelman, Allan, Pachter, Nicholas, Ahmed, Munaza, Ramon y Cajal, Teresa, Zgajnar, Janez, Brewer, Carole, Gadea, Neus, Brady, Angela F., van Os, Theo, Gallagher, David, Johannsson, Oskar, Donaldson, Alan, Barwell, Julian, Nicolai, Nicola, Friedman, Eitan, Obeid, Elias, Greenhalgh, Lynn, Murthy, Vedang, Copakova, Lucia, Saya, Sibel, McGrath, John, Cooke, Peter, Rønlund, Karina, Richardson, Kate, Henderson, Alex, Teo, Soo H., Arun, Banu, Kast, Karin, Dias, Alexander, Aaronson, Neil K., Ardern-Jones, Audrey, Bangma, Chris H., Castro, Elena, Dearnaley, David, Eccles, Diana M., Tricker, Karen, Eyfjord, Jorunn, Falconer, Alison, Foster, Christopher, Gronberg, Henrik, Hamdy, Freddie C., Stefansdottir, Vigdis, Khoo, Vincent, Lindeman, Geoffrey J., Lubinski, Jan, Axcrona, Karol, Mikropoulos, Christos, Mitra, Anita, Moynihan, Clare, Rennert, Gadi, Suri, Mohnish, Wilson, Penny, Dudderidge, Tim, Offman, Judith, Kote-Jarai, Zsofia, Vickers, Andrew, Lilja, Hans, Eeles, Rosalind A.
    Publicado 2019
    “…The 4 kallikrein marker model discriminated better (area under the curve [AUC] = 0.73) for clinically significant cancer at biopsy than PSA alone (AUC = 0.65). …”
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    Interim Results from the IMPACT Study: Evidence for Prostate-specific Antigen Screening in BRCA2 Mutation Carriers
  7. 74987
    “…SMOC1, YWHAZ, ALDOA and MAP1B emerged as biomarker candidates that could best discriminate between individuals with AD and non-AD cognitive impairment as well as Tau/β-amyloid ratio. …”
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    Targeted mass spectrometry to quantify brain-derived cerebrospinal fluid biomarkers in Alzheimer’s disease
  8. 74988
    “…We studied the glycosylation of the brain sAPPα and sAPPβ using lectins and pan-specific antibodies to discriminate between the fragments originated from neuronal APP695 and glial/KPI variants. …”
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    Amyloid precursor protein glycosylation is altered in the brain of patients with Alzheimer’s disease
  9. 74989
    “…Lastly, we incorporated three machine learning models to differentiate RA from HC and OA, and performed feature selection to determine discriminative features. Experimental results showed that our proposed method achieved an area under the receiver operating characteristic curve of 0.92, which demonstrated that our selected autoantibodies combined with machine learning can efficiently detect RA. …”
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    Isotypes of autoantibodies against novel differential 4-hydroxy-2-nonenal-modified peptide adducts in serum is associated with rheumatoid arthritis in Taiwanese women
  10. 74990
    “…The number of UD on the digital RFFT was associated with higher education (Spearman’s r = 0.43, p < 0.001), and younger age (Pearson’s r = − 0.36, p < 0.001), showing its ability to discriminate between different age categories and levels of education. …”
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    The validity and reliability of a digital Ruff Figural Fluency Test (RFFT)
  11. 74991
    “…While the accuracy of regional LS was similar, CS by one software was less accurate (AUC 0.68) than tagging (AUC 0.80, p < 0.006) and RS less accurate (AUC 0.578) than the other two (AUC 0.76 and 0.73, p < 0.02) to discriminate segments with LGE. CONCLUSIONS: We confirm good agreement of CMR FT and little intervendor difference for GLS and GCS evaluation, with variable agreement for GRS. …”
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    Multivendor comparison of global and regional 2D cardiovascular magnetic resonance feature tracking strains vs tissue tagging at 3T
  12. 74992
    “…Cutoff of 15 AU/mL was assumed to discriminate responders to vaccination with a protective titer. …”
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    Fifth-week immunogenicity and safety of anti-SARS-CoV-2 BNT162b2 vaccine in patients with multiple myeloma and myeloproliferative malignancies on active treatment: preliminary data from a single institution
  13. 74993
    “…Gender seems to be a discriminating variable with women scoring significantly higher than man, both for anxiety symptoms (H (1) = 82.91, p < .001) and all dimensions of PTSD symptoms (intrusion H (1) = 71.23, p < .001, avoidance H (1) = 61.28, p < .001), and hyperarousal (H (1) = 67.348, p < .001). …”
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    Incidence of PTSD and generalized anxiety symptoms during the first wave of COVID-19 outbreak: an exploratory study of a large sample of the Italian population
  14. 74994
    “…A total of 74 ICP elevations ≥ 20 mmHg occurred in seven patients. Best discriminative thresholds for ICP elevation were: CV < 0.8 mm/s (AUC 0.740), per-change < 10% (AUC 0.743), DV < 0.2 mm/s (AUC 0.703), and Lat > 0.3 s (AUC 0.616). …”
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    Automated Pupillometry Identifies Absence of Intracranial Pressure Elevation in Intracerebral Hemorrhage Patients
  15. 74995
    “…BACKGROUND: The aim of this study was to validate whether regional ventilation and perfusion data measured by electrical impedance tomography (EIT) with saline bolus could discriminate three broad acute respiratory failure (ARF) etiologies. …”
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    Three broad classifications of acute respiratory failure etiologies based on regional ventilation and perfusion by electrical impedance tomography: a hypothesis-generating study
  16. 74996
    “…Progranulin showed high discriminative power to differentiate bacterial CAP from COVID-19 (sensitivity 0.91, specificity 0.94, AUC 0.91 (CI = 0.8–1.0) and performed significantly better than PCT, IL-6 and CRP. …”
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    Progranulin signaling in sepsis, community-acquired bacterial pneumonia and COVID-19: a comparative, observational study
  17. 74997
    “…Subsequently, we identified a cluster of metabolites that strongly associated with future T2D risk from which we developed a predictive metabolic signature with a discriminating power (AUC) of 0.78, superior to common clinical variables (i.e., fasting glucose, AUC 0.56 or 2-h glucose, AUC 0.62). …”
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    Intensive lactation among women with recent gestational diabetes significantly alters the early postpartum circulating lipid profile: the SWIFT study
  18. 74998
    “…In total, 3 of the 4 hypotheses were accepted and 44% of the items showed satisfying known-group discriminative validity. CONCLUSION: A renal disease questionnaire used for clinical decision-making in outpatient follow-up showed acceptable content validity and substantial to almost perfect reliability. …”
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    Patient-reported outcome measures for clinical decision-making in outpatient follow-up: validity and reliability of a renal disease questionnaire
  19. 74999
    “…The reliability (internal consistency, test–retest), construct validity (convergent and discriminant validity, known-groups methods), and responsiveness (Guyatt's responsiveness statistic [GRS]) of the PICQ scores were evaluated. …”
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    Development of the Presbyopia Impact and Coping Questionnaire
  20. 75000
    “…The screening model can effectively discriminate HCC patients from non-HCC controls, including liver cirrhotic patients, asymptomatic HBsAg+ and healthy individuals, achieving an AUC of 0.957(95% CI 0.939–0.975), whereas serum α-fetoprotein (AFP) only achieved an AUC of 0.803 (95% CI 0.758–0.847). …”
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    Cell-free DNA methylation markers for differential diagnosis of hepatocellular carcinoma
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