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  1. 73661
    “…The receiver operating characteristic (ROC) curve’s area under the curve (AUC) and the calibration curve’s respective curves were used to further validate the model’s calibration with predictability and discriminative capabilities. By using decision curve analysis, the resulting model’s clinical effectiveness was assessed. …”
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  2. 73662
    “…With a lower residual and root mean square error and a larger area under the curve (AUC = 0.951), the Random forest machine model showed the greatest discriminative performance. The resulting random forest model was based on five genes, and it performed well (AUC = 0.894) on external validation datasets. …”
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  3. 73663
    “…Receiver operating characteristic (ROC) analyses and the area under the ROC curve (AUC) were then used to assess the ability of UA, UA/CR, and UHR to discriminate kidney impairment in T2DM. RESULTS: UA and UHR demonstrated a positive association with DKD risk (P < 0.05). …”
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  4. 73664
    “…The C-index of 0.852 (0.842-0.862) in the training cohort and 0.876 (0.868-0.892) in the testing cohort indicated satisfactory discriminative ability of the nomogram. Calibration plots showed favorable consistency between the nomogram predictions and actual observations in both the training and validation cohorts. …”
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  5. 73665
    “…RESULTS: Our results indicate that local communities in Kakamega were able to discriminate between six different and scientifically recognized stingless bee species, and they provided detailed accounts on the species-specific non-food uses of these SBH. …”
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  6. 73666
    “…We aimed to accurately evaluate the discriminative ability of the three tests administrated at the same visit simultaneously in detecting AD and MCI due to AD in the absence of a gold standard. …”
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  7. 73667
    “…RESULTS: TNM stages, S2 and S3 were identified as the significant prognosis factors for both OS and PFS; S2 (OS: (HR (95%), 2.26 (1.40–3.67); PFS: (HR (95%), 2.23 (1.36–3.65)) demonstrated the best ability in discriminating patients with over- and under-expression. …”
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  8. 73668
  9. 73669
    “…Compared to the random forest, Adaboost, and decision tree models, the least absolute shrinkage and selection operator logistic regression model showed good internal discriminative accuracy (internal AUC 0.723, 95% CI 0.701-0.744) and transportability (external AUC 0.703, 95% CI 0.692-0.714) for the primary outcome. …”
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  10. 73670
    “…METHODS: This cross-sectional study used a pilot-tested, content-validated, multi-linguistic online survey within Europe, distributed through a non-discriminative snowball sampling method. Inclusion was limited to facilitators observing behavioural skills within a medical team setting. …”
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  11. 73671
  12. 73672
    “…Moreover, lnLAEI accurately identified PCWP ≥ 15 mmHg (AUC = 0.939, p < 0.001), and the optimal cut-off identified (lnLAEI ≤ 3.85) in the derivation cohort discriminated PCWP ≥ 15 mmHg with 82.4% sensitivity, 88.2% specificity, and 85.3% accuracy in the validation cohort. …”
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  13. 73673
  14. 73674
    “…Specifically, we will determine whether there are morphometric and functional changes associated with participation in MBSR in brain regions related to meta-awareness, body awareness, memory consolidation-reconsolidation, emotion regulation and in networks postulated to underpin the sensory-discriminative, cognitive-evaluative and affective-motivational aspects of the pain experience. …”
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  15. 73675
  16. 73676
    “…Based on moss data collected in 2010/2011 throughout Europe and data on total atmospheric deposition modelled by two chemical transport models (EMEP MSC-E, LOTOS-EUROS), correlation coefficients between concentrations of heavy metals in moss and in modelled atmospheric deposition were specified for spatial subsamples defined by ecological land classes of Europe (ELCE) as a spatial reference system. Linear discriminant analysis (LDA) and logistic regression (LR) were then used to separate moss sampling sites regarding their contribution to the strength of correlation considering the areal percentage of urban, agricultural and forestry land use around the sampling location. …”
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  17. 73677
    “…TP42/40 ratio alone identified Aβ-PET status with an area under the curve (AUC) of 0.881 (95% confidence interval [CI] = 0.779–0.982). Discriminating performance of TP42/40 to detect Aβ-PET-positive subjects yielded sensitivity and specificity values at Youden’s cutoff of 77.8% and 87.5%, respectively, with a positive predictive value of 0.732 and negative predictive value of 0.900. …”
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  18. 73678
  19. 73679
  20. 73680
    “…Through online software prediction, miR-29c-3p has been discriminated as the target miRNA of LINC01296 for further research, and subsequent luciferase assay confirmed bioinformatics prediction. …”
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