They reveal our decision-making processes, requiring that we justify each step in a treatment course. Patients seen in clinical practice rarely fit the artificial standards imposed for selection into controlled research trials. These methods may be applied to address the treatment of Axis II personality dimensions. This article represents an initial offering of treatment algorithms for personality dimensions, based on literature review and the author's clinical experience. These algorithms are intended to promote interest, discussion, and research, which, it is hoped, will lead to formation of expert panels to further refine the algorithms.
Algorithms are rule-based systems.
To be most broadly applicable and most conservative, the algorithms assume treatment in an outpatient setting. Well-controlled clinical trials with negative outcomes reduce the overall level of a recommendation based on several positive outcome studies. There are several important limitations to this method. Given the diversity in methods, patient characteristics, and assessment measures found in the treatment literature, one can generalize claims for efficacy only after multiple replications. Statistically significant differences in research measures do not always translate into clinically important differences between patients on drug and placebo.