Search PubMed⌕ Search

PubMed · 17096201

Reflections on accuracy.

Abstract

The difference between test accuracy and predictive accuracy is presented and defined. The failure to distinguish between these two types of measures is shown to have led to a misguided debate over the interpretation of prevalence estimates. The distinction between test accuracy defined as sensitivity and specificity, and predictive accuracy defined as positive and negative predictive value is shown to reflect the choice of the denominator used to calculate true positive, false positive, false negative, and true negative rates. It is further shown that any instrument will tend to overestimate prevalence in low base rate populations and underestimate it in those populations where prevalence is high. The implications of these observations are then discussed in terms of the need to define diagnostic thresholds that have clinical and policy relevance.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Blasé Gambino. 2006. Reflections on accuracy.. https://doi.org/10.1007/s10899-006-9025-5

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Cross-ancestry meta-analysis of opioid use disorder uncovers novel loci with predominant effects in brain regions associated with addiction.

Despite an estimated heritability of ~50%, genome-wide association studies of opioid use disorder (OUD) have revealed few genome-wide significant loci. We conducted a cross-ancestry meta-analysis of OUD in the Million Veteran Program (N = 425,944). In addition to known exonic variants in OPRM1 and FURIN, we identified intronic variants in RABEPK, FBXW4, NCAM1 and KCNN1. A meta-analysis including other datasets identified a locus in TSNARE1. In total, we identified 14 loci for OUD, 12 of which are novel. Significant genetic correlations were identified for 127 traits, including psychiatric disorders and other substance use-related traits. The only significantly enriched cell-type group was CNS, with gene expression enrichment in brain regions previously associated with substance use disorders. These findings increase our understanding of the biological basis of OUD and provide further evidence that it is a brain disease, which may help to reduce stigma and inform efforts to address the opioid epidemic.

Behavior, Addictive↗

[Compulsive shopping--current considerations on classification and therapy].

Compulsive shopping is classified by ICD-10 (F63.8) as an "impulse control disorder, not otherwise classified". Several authors consider compulsive shopping rather as a variety of dependence disorder. It is characterized by the impulsive or compulsive buying of unneeded things, personal distress, impaired social and vocational functioning, and/or financial problems. In this case, we discuss a two-way therapy consisting of addiction-specific psychological education and high dose selective serotonin reuptake inhibitors (SSRIs). We further point to compliance problems caused by SSRI side effects.

Behavior, Addictive↗

Frequent card playing and pathological gambling: the utility of the Georgia Gambling Task and Iowa Gambling Task for predicting pathology.

The current investigation examined performance on two laboratory-based gambling tasks, the Georgia Gambling Task (GGT; Goodie, 2003. The effects of control on betting: Paradoxical betting on items of high confidence with low value. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 598-610) and the Iowa Gambling Task (IGT; Bechara, Damasio, Damasio, & Anderson, 1994. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50, 7-15), as well as self-reported markers of gambling pathology using the Diagnostic Interview for Gambling Severity (DIGS; Winters, Specker, & Stinchfield, 2002. The downside: Problem and pathological gambling (pp. 143-148). Reno, NV: University of Nevada, Reno) among a sample of undergraduate students who are frequent card players. Two hundred twenty-one participants (55 female and 166 male; mean age 19.21 years) who self-classified as playing cards at least once per month completed these measures. Performance on GGT and IGT systematically related to gambling-related pathology in several ways. Overconfidence and bet acceptance on the GGT, and myopic focus on reward on the IGT, predicted gambling related pathology. GGT and IGT performance correlated with each other, but both contributed independently to predicting gambling pathology. Card playing frequency predicted gambling pathology but not GGT or IGT performance. Discussion focuses on the role of biases of judgment and risky decision making in pathological gambling.

Behavior, Addictive↗