A widely-accepted notion is that organization can improve task performance and generally allow us to better functionwithin a given task environment (Kirsh, 1995; 1996). However, it remains unclear the extent to which individuals believe thatorganization will help to improve task performance when they are asked to carry out mundane tasks in the real world. Toexamine this, individuals were asked to search through a pile of Lego building blocks for specific pieces. Prior to the searchtask, they were asked their preferred strategy for this task (e.g., organizing vs. not organizing the Lego pile prior to search) andto estimate how much time and effort each strategy would take for task completion. While both strategies were comparable interms of objective task completion time and subjective time and effort estimations, participants were overwhelmingly biasedagainst choosing the organization strategy. Implications for the current study will be discussed.

Objective

We previously demonstrated the utility of the Automated Neuropsychological Assessment Metrics (ANAM) for screening cognitive impairment (CI) in patients with systemic lupus erythematosus (SLE) and developed composite indices for interpreting ANAM results. Our objectives here were to provide further support for the ANAM's concurrent criterion validity against the American College of Rheumatology neuropsychological battery (ACR-NB), identify the most discriminatory subtests and scores of the ANAM for predicting CI, and provide a new approach to interpret ANAM results using Classification and Regression Tree (CART) analysis.

Methods

300 adult SLE patients completed an adapted ACR-NB and ANAM on the same day. As per objectives, six models were built using combinations of ANAM subtests and scores and submitted to CART analysis. Area under the curve (AUC) was calculated to evaluate the ANAM's criterion validity compared to the adapted ACR-NB; the most discriminatory ANAM subtests and scores in each model were selected, and performance of models with the highest AUCs were compared to our previous composite indices; decision trees were generated for models with the highest AUCs.

Results

Two models had excellent AUCs of 86 and 89%. Eight most discriminatory ANAM subtests and scores were identified. Both models demonstrated higher AUCs against our previous composite indices. An adapted decision tree was created to simplify the interpretation of ANAM results.

Conclusion

We provide further validity evidence for the ANAM as a valid CI screening tool in SLE. The decision tree improves interpretation of ANAM results, enhancing clinical utility.