The selection and use of diagnostic categories in clinical counseling.

1948 ◽  
Vol 12 (5) ◽  
pp. 362-362
Author(s):  
No authorship indicated
2017 ◽  
Vol 225 (3) ◽  
pp. 175-188 ◽  
Author(s):  
Peter J. Lang ◽  
Lisa M. McTeague ◽  
Margaret M. Bradley

Abstract. Several decades of research are reviewed, assessing patterns of psychophysiological reactivity in anxiety patients responding to a fear/threat imagery challenge. Findings show substantive differences in these measures within principal diagnostic categories, questioning the reliability and categorical specificity of current diagnostic systems. Following a new research framework (US National Institute of Mental Health [NIMH], Research Domain Criteria [RDoC]; Cuthbert & Insel, 2013 ), dimensional patterns of physiological reactivity are explored in a large sample of anxiety and mood disorder patients. Patients’ responses (e.g., startle reflex, heart rate) during fear/threat imagery varied significantly with higher questionnaire measured “negative affect,” stress history, and overall life dysfunction – bio-marking disorder groups, independent of Diagnostic and Statistical Manuals (DSM). The review concludes with a description of new research, currently underway, exploring brain function indices (structure activation, circuit connectivity) as potential biological classifiers (collectively with the reflex physiology) of anxiety and mood pathology.


1981 ◽  
Vol 20 (03) ◽  
pp. 163-168 ◽  
Author(s):  
G. Llndberg

A system for probabilistic diagnosis of jaundice has been used for studying the effects of taking into account the unreliability of diagnostic data caused by observer variation. Fourteen features from history and physical examination were studied. Bayes’ theorem was used for calculating the probabilities of a patient’s belonging to each of four diagnostic categories.The construction sample consisted of 61 patients. An equal number of patients were tested in the evaluation sample. Observer variation on the fourteen features had been assessed in two previous studies. The use of kappa-statistics for measuring observer variation allowed the construction of a probability transition matrix for each feature. Diagnostic probabilities could then be calculated with and without the inclusion of weights for observer variation. Tests of system performance revealed that discriminatory power remained unchanged. However, the predictions rendered by the variation-weighted system were diffident. It is concluded that taking observer variation into account may weaken the sharpness of probabilistic diagnosis but it may also help to explain the value of probabilistic diagnosis in future applications.


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