receiver operating characteristic

(redirected from ROC plot)

re·ceiv·er op·er·at·ing char·ac·ter·is·tic (ROC),

a plot of the sensitivity of a diagnostic test as a function of nonspecificity (one minus the specificity). The ROC curve indicates the intrinsic properties of a test's diagnostic performance and can be used to compare the relative merits of competing procedures.
Farlex Partner Medical Dictionary © Farlex 2012


(kar?ak-te-ris'tik) [Gr. charakteristikos, pert. to a stamp]
1. A trait or character typical of an organism or of an individual.
2. In logarithmic expressions, the number to the left of the decimal point, as distinguished from the mantissa, the number to the right of the decimal point.

acquired characteristic

A trait or quality that was not inherited but is the result of environmental influence.

anal characteristic

Anal personality.

dominant characteristic

See: dominant

primary sex characteristic

An inherited trait that influences the development of the reproductive organs.

receiver operating characteristic

Receiver operating curve.

recessive characteristic

Recessive gene.

secondary sex characteristic

A gender-related physical attribute that normally develops under the influence of sex hormones at puberty. Voice quality, facial hair, and body fat distribution are examples.

sex-conditioned characteristic

A genetic trait carried by both sexes but expressed or inhibited by the sex of the individual.

sex-limited characteristic

A trait present in only one sex even though the gene responsible is present in both sexes.

sex-linked characteristic

A trait controlled by genes on the sex chromosomes. The X and Y chromosomes determine sex but also carry genes unrelated to sex.
Synonym: sex-linked gene
Medical Dictionary, © 2009 Farlex and Partners
References in periodicals archive ?
A commonly used index which minimizes the distance between the ROC plot and the point (0,1) [or upper left most corner] was used to identify the optimal breakpoint for low systolic BP for the optimal classification of mortality.
To determine goodness of fit for the two models with test data we examined the model's discriminatory ability by measuring the AUC of the ROC plot (Baldwin 2009).
The x axis of the ROC plot displays the (1--specificity) obtained in the studies in the review and the y axis shows the corresponding sensitivity.
The ROC plot in Figure la contains four operating characteristics that correspond to the mean z-scores for overall session hits (H) and false alarms (FA) in the four signal probability conditions in Experiment 1.
Our primary analysis was based on ROC plot and stepwise multiple linear regression analyses, which do not depend on selecting cutoffs for the dependent markers.
Because the data-driven approach specifically selects the cutoff value with the highest sum of sensitivity and specificity (i.e., closest to the top left corner of the ROC plot), this value is generally a point above the true underlying ROC curve.
The Graphs worksheet is where the summary ROC plot appears.
ROC plot analysis (4) was used to assess the accuracy of the OPC test and to compare it with ECLIA detection in 60 serum samples from patients.
To determine the diagnostic accuracy of the two assays for CHF, we performed ROC plot analysis, and areas under the curve (AUC) were calculated for both BNP assays.
Our analysis indicated that an almost-perfect ROC plot was obtained if the clinical diagnosis of APS was based on four or more features.
One approach to estimating the diagnostic accuracy of a test where multiple studies have different conclusions is to combine the claimed sensitivities and specificities of all credible studies into a summary ROC plot (66,67).