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We assessed discrimination using the area under the curve (AUC) of the receiver operating characteristics (ROC) curve.
We assessed discrimination, validation and calibration of our model.
We assessed discrimination of the model with area under the receiver operating curve (AUC ± SE = 0.79 ± 0.03 with 95% C.I. 0.74 to 0.84; Chi Square statistics: P <0.001).
We assessed discrimination of the baseline, genetic, and combined models; improvement in discrimination; and clinical usefulness of updating the baseline model with genetic variants.
We assessed discrimination (the model's ability to distinguish survivors from non-survivors) by concordance statistic (c-statistic) using the area under the receiver operating characteristic curve (AUC).
We also assessed discrimination (i.e., the ability of the model to separate survivors and non-survivors) using the area under the curve (AUC) of the receiver-operating characteristics (ROC) curve.
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Two methods of assessing discrimination ability were adopted.
As re-smelling is not permitted, an un-intended STM load may generate, even though the task purports to assess discrimination ability.
The area under the receiver operating characteristic (ROC) curve was used to assess discrimination; an area under the ROC curve of 1.0 denotes perfect discrimination, whereas a value close to 0.50 indicates no apparent accuracy.
Score performance was judged by assessing discrimination and calibration and using the area under receiver operating characteristics (ROC) curve and by the Hosmer Lemeshow goodness-of-fit test, respectively.
Area under the receiver-operating-characteristic curve (AUROC) and slope and intercept of the calibration curve were used to assess discrimination and calibration of the CRASH (Corticosteroid-Randomization-After-Significant-Head injury) and IMPACT (International-Mission-For-Prognosis-And-Clinical-Trial) models, respectively.
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