The influence of different error estimates in the detection of post-operative cognitive dysfunction using reliable change indices with correction for practice effects.

The reliable change index (RCI) expresses change relative to its associated error, and is useful in the identification of post-operative cognitive dysfunction (POCD). This paper examines four common RCIs that each account for error in different ways. Three rules incorporate a constant correction for practice effects and are contrasted with the standard RCI that had no correction for practice. These rules are applied to 160 patients undergoing coronary artery bypass graft (CABG) surgery who completed neuropsychological assessments preoperatively and 1 week post-operatively using error and reliability data from a comparable healthy non-surgical control group. The rules all identify POCD in a similar proportion of patients, but the use of the within subject standard deviation, expressing the effects of random error, as an error estimate is a theoretically appropriate denominator when a constant error correction, removing the effects of systematic error, is deducted from the numerator in a RCI.     

A new index for evaluating horizontal skeletal discrepancies and predicting treatment outcomes

The cephalometric measurement known as Wits appraisal (AOBO) was studied to determine its importance in diagnosis, treatment, and prediction of treatment success. The Wits appraisal is used primarily to identify unreliable ANB values (those that do not accurately reflect the degree of horizontal jaw discrepancy, ie, those that differ greatly from the AOBO value). Therefore, differences between AOBO and ANB also were evaluated. Because neither measurement alone can assess jaw discrepancies with total accuracy, they were combined, plotted on a scattergram, and divided into equal zones to create a new index of horizontal skeletal discrepancies and treatment predictability. Pretreatment values correspond to treatment difficulty, and posttreatment values correspond to the effectiveness of treatment. Treatment time for each zone also was evaluated. We were able to draw several conclusions from this study. First, combining AOBO and ANB values results in a more accurate measurement of treatment difficulty. These combined values also can be used to predict both treatment time and the success of treatment for horizontal discrepancies. Second, the AOBO value predicts variations in treatment time at each ANB value. Finally, adding ANB and AOBO values and then dividing the total yields zones that represent statistically significant differences in treatment times and percentages of success. The zones also correlate with the difference between AOBO and ANB measurements.     

Two approaches to social support in smoking cessation: Commodity model and nondirective support

The commodity model of social support and features of Nondirective Support may clarify varied results of support interventions for smoking cessation. A commodity model views social support as attractive in and of itself and as an alternative to high-risk choices such as smoking. If such support is easily accessible, a would-be quitter is less likely to choose to smoke. Consistent with the commodity model, social support interventions tend to be effective as long as support remains available, but they lose their effects when support is terminated. From a second approach, Nondirective Support entails accepting recipients' goals, cooperating without taking control, and validating recipients' feelings. In contrast, Directive Support entails taking control and telling recipients what to do and feel. Review of support interventions indicates the value of the continued availability of support suggested by the commodity model and of Nondirective Support's flexibility and responsiveness to the person.     

Improved graft survival for flow cytometry and antihuman globulin crossmatch-negative retransplant recipients

We compared our standard NIH (extended incubation) crossmatch (XM) with antihuman globulin (AHG) and flow cytometry XMs and correlated the results with rejection episodes and graft survivals. For 89 CsA-Pred, primary renal allograft recipients, AHG and/or FCXM results did not improve on the NIH-XM-negative (NEG) graft survival results, whether testing pretransplant or historical (Hx) sera. Similarly, there was no association of a positive (POS) AHG or FCXM with increased rejection episodes in these primary recipients. However, for retransplant (Re-Tx) recipients a neg AHG or FCXM did discriminate fewer rejections and an improved graft survival compared with the NIH-XM-neg. results. The overall one-year graft survival for the 47 Re-Tx recipients studied herein was 66% (based on a neg pre-Tx NIH-XM). Pre-Tx AHG-NEG, Re-Tx recipients displayed an improved graft survival compared with NIH-XM NEG recipients (77% vs. 66%, P less than 0.05) and with AHG-POS recipients (77% vs. 47%, P less than 0.05). Similarly, pre-Tx, FCXM-NEG, Re-Tx recipients displayed improved graft survivals compared with NIH-XM-NEG recipients (83% vs. 66%, P less than 0.05) and FCXM-POS recipients (83% vs. 48%, P less than 0.05). Re-Tx recipients displaying a POS AHG and/or FCXM experienced a significantly greater number of rejections than NEG-XM recipients (P less than 0.05, respectively). The AHG and FCXM results correlated with rejections and graft survivals whether testing pre-Tx or Hx high-PRA sera. Re-Tx recipients who were AHG-XM-NEG but FCXM-POS, experienced more rejection episodes than recipients who displayed a negative XM reactivity for both AHG and FCXM (P less than 0.02), but with no resulting differences in graft survival. HLA matching, pre-Tx blood transfusions and PRA did not impact on these crossmatch and graft survival results. Use of AHG and/or FCXMs for Re-Tx, but not primary, recipients should help to improve graft survival for these high-risk recipients.