Rostrolateral prefrontal cortex: Domain‐general or domain‐sensitive?

The ability to jointly consider several structured mental representations, or relations, is fundamental to human cognition. Prior studies have consistently linked this capacity for relational integration to rostrolateral prefrontal cortex (RLPFC). Here, we sought to test two competing hypotheses: (1) RLPFC processes relations in a domain-general manner, interacting with different brain regions as a function of the type of lower-level relations that must be integrated; or (2) A dorsal-ventral gradient exists within RLPFC, such that relational integration in the visuospatial domain involves relatively more dorsal RLPFC than integration in the semantic domain. To this end, we examined patterns of fMRI activation and functional connectivity during performance of visuospatial and semantic variants of a relational matching task. Across the two task variants, the regions that were most strongly engaged during relational comparison were left RLPFC and left intraparietal sulcus (IPS). Within left RLPFC, there was considerable overlap in activation for the semantic and visuospatial tasks. However, visuospatial task activation peaks were located dorsally to the semantic task peaks. In addition, RLPFC exhibited differential functional connectivity on the two tasks, interacting with different brain regions as a function of the type of relations being compared. While neurons throughout RLPFC may share the function of integrating diverse inputs, individual RLPFC neurons may have privileged access to particular representations depending on their anatomical inputs, organized along a dorsal-ventral gradient. Thus, RLPFC is well-positioned as a locus of abstraction from concrete, domain-specific details to the general principles and rules that enable higher-level cognition.     

Evaluating health information technology in community-based settings: lessons learned

Implementing health information technology (IT) at the community level is a national priority to help improve healthcare quality, safety, and efficiency. However, community-based organizations implementing health IT may not have expertise in evaluation. This study describes lessons learned from experience as a multi-institutional academic collaborative established to provide independent evaluation of community-based health IT initiatives. The authors'' experience derived from adapting the principles of community-based participatory research to the field of health IT. To assist other researchers, the lessons learned under four themes are presented: (A) the structure of the partnership between academic investigators and the community; (B) communication issues; (C) the relationship between implementation timing and evaluation studies; and (D) study methodology. These lessons represent practical recommendations for researchers interested in pursuing similar collaborations.     

Fast and slow thinking; and the problem of conflating clinical reasoning and ethical deliberation in acute decision‐making

Expertise in a medical specialty requires countless hours of learning and practice and a combination of neural plasticity and contextual case experience resulting in advanced gestalt clinical reasoning. This holistic thinking assimilates complex segmented information and is advantageous for timely clinical decision‐making in the emergency department and paediatric or neonatal intensive care units. However, the same agile reasoning that is essential acutely may be at odds with the slow deliberative thought required for ethical reasoning and weighing the probability of patient morbidity. Recent studies suggest that inadequate ethical decision‐making results in increased morbidity for patients and that clinical ethics consultation may reduce the inappropriate use of life‐sustaining treatment. Behavioural psychology research suggests there are two systems of thinking – fast and slow – that control our thoughts and therefore our actions. The problem for experienced clinicians is that fast thinking, which is instinctual and reflexive, is particularly vulnerable to experiential biases or assumptions. While it has significant utility for clinical reasoning when timely life and death decisions are crucial, I contend it may simultaneously undermine the deliberative slow thought required for ethical reasoning to determine appropriate therapeutic interventions that reduce future patient morbidity. Whilst health‐care providers generally make excellent therapeutic choices leading to good outcomes, a type of substitutive thinking that conflates clinical reasoning and ethical deliberation in acute decision‐making may impinge on therapeutic relationships, have adverse effects on patient outcomes and inflict lifelong burdens on some children and their families.     

Use of Case-Based Reasoning to Enhance Intensive Management of Patients on Insulin Pump Therapy

Background

This study was conducted to develop case-based decision support software to improve glucose control in patients with type 1 diabetes mellitus (T1DM) on insulin pump therapy. While the benefits of good glucose control are well known, achieving and maintaining good glucose control remains a difficult task. Case-based decision support software may assist by recalling past problems in glucose control and their associated therapeutic adjustments.

Methods

Twenty patients with T1DM on insulin pumps were enrolled in a 6-week study. Subjects performed self-glucose monitoring and provided daily logs via the Internet, tracking insulin dosages, work, sleep, exercise, meals, stress, illness, menstrual cycles, infusion set changes, pump problems, hypoglycemic episodes, and other events. Subjects wore a continuous glucose monitoring system at weeks 1, 3, and 6. Clinical data were interpreted by physicians, who explained the relationship between life events and observed glucose patterns as well as treatment rationales to knowledge engineers. Knowledge engineers built a prototypical system that contained cases of problems in glucose control together with their associated solutions.

Results

Twelve patients completed the study. Fifty cases of clinical problems and solutions were developed and stored in a case base. The prototypical system detected 12 distinct types of clinical problems. It displayed the stored problems that are most similar to the problems detected, and offered learned solutions as decision support to the physician.

Conclusions

This software can screen large volumes of clinical data and glucose levels from patients with T1DM, identify clinical problems, and offer solutions. It has potential application in managing all forms of diabetes.     

Tuberculous meningitis: does lowering the treatment threshold result in many more treated patients?

Objective  To determine how many more patients would be treated when lowering the treatment threshold for tuberculous meningitis.
Methods  From 1989 to 2004 findings of patients with symptoms lasting more than 1 week and inflammatory changes of cerebrospinal fluid (CSF) were collected. Several models of latent class analysis were tested. Cumulative numbers of cases were plotted against different cut-offs for post-test probability.
Results  In a cohort of 232 patients the prevalence of tuberculous meningitis (TBM) was estimated at 79.8% (95% CI. 67,0–88,1); probabilities above 80% were reached in 73% of patients. Lowering this threshold from 80% to 20% would add 14% more patients to be treated, for a total of 87%. A further lowering of the threshold to 5% would imply 5% more patients to be treated, bringing the cumulative number to 92%. The difference of lowering the threshold from 80% to 5% was 19%.
Conclusion  In this setting, at least 75% of patients showing suggestive symptoms for more than a week and CSF changes very probably had TBM. The number of patients that should be treated does not increase linearly when lowering the threshold.     

P300 and positive slow waves reveal the plausibility in inductive reasoning

Category‐based induction is an advanced cognitive function that is based on our category‐level knowledge. Previous findings have recognized the distance effect in category‐based induction: Inductive strength is affected by the hierarchical distance between the premises and conclusions. However, the neural mechanisms underlying this effect require elucidation. In the present study, we investigated the neural mechanisms of the distance effect by using EEG technology and a new experimental paradigm—category‐based induction. In this paradigm, we used three hierarchical levels of categories—the subordinate category, the basic category, the superordinate category—and an irrelevant category. We further used these categories to create four types of trial that varied in the hierarchical distance between the premise and the conclusion: the subordinate‐basic, the basic‐superordinate, the subordinate‐superordinate, and the irrelevant‐superordinate trials. In each trial, participants judged the probability that the conclusion category had the same property as the premise category. Our behavioral results revealed that people responded more slowly in the irrelevant‐superordinate trials than in the basic‐superordinate and the subordinate‐basic trials. Our ERP results showed that the irrelevant‐superordinate trials elicited smaller P300 (250–500 ms) amplitudes than did the subordinate‐basic and the basic‐superordinate trials. In addition, the subordinate‐superordinate trials elicited smaller P300 and PSW (700–998 ms) amplitudes than did the subordinate‐basic and the basic‐superordinate combinations. These findings indicate that the amplitudes of P300 and PSW may reflect the distance effect in inductive reasoning: The further the premise‐conclusion hierarchical distance, the lower the inductive strength, and thus the smaller the P300 and PSW amplitudes.     

Line of reasoning as a representation of nurses' clinical decision making

Line of reasoning (LOR) is offered as an alternative representation of clinical decision making for studies using protocol analysis. A LOR is defined as an argument or set of arguments leading to a conclusion. Because LOR combines both knowledge and cognitive processes, it provides a more complete representation of how a person uses knowledge to make a decision in a particular situation than do other representations. Operationalization of LOR in the form of templates and narratives enhances systematic data interpretation and coding. The use of LOR as a representation is illustrated in a study of critical care nurses' clinical decision making, specifically the determination of a patient's readiness to wean from mechanical ventilation. © 1997 John Wiley & Sons, Inc. Res Nurs Health 20: 353–364, 1997