Assessment of immediate conservative breast surgery reconstruction: A classification system of defects revisited and an algorithm for selecting the appropriate technique

Background: Although various techniques have been used for breast conservation surgery reconstruction, there are few studies describing a logical approach to reconstruction of these defects. The objectives of this study were to establish a classification system for partial breast defects and to develop a reconstructive algorithm. Methods: The authors reviewed a 7-year experience with 209 immediate breast conservation surgery reconstructions. Mean follow-up was 31 months. Type I defects include tissue resection in smaller breasts (bra size A/B), including type IA, which involves minimal defects that do not cause distortion; type III, which involves moderate defects that cause moderate distortion; and type IC, which involves large defects that cause significant deformities. Type II includes tissue resection in medium-sized breasts with or without ptosis (bra size C), and type III includes tissue resection in large breasts with ptosis (bra size D). Results: Eighteen percent of patients presented type I, where a lateral thoracodorsal flap and a latissimus dorsi flap were performed in 68 percent. Forty-five percent presented type II defects, where bilateral mastopexy was performed in 52 percent. Thirty-seven percent of patients presented type III distortion, where bilateral reduction mammaplasty was performed in 67 percent. Thirty-five percent of patients presented complications, and most were minor. Conclusions: An algorithm based on breast size in relation to tumor location and extension of resection can be followed to determine the best approach to reconstruction. The authors` results have demonstrated that the complications were similar to those in other clinical series. Success depends on patient selection, coordinated planning with the oncologic surgeon, and careful intraoperative management.

Decision-Making Deficits Linked to Real-life Social Dysfunction in Crack Cocaine-Dependent Individuals

Crack cocaine-dependent individuals (CCDI) present abnormalities in both social adjustment and decision making, but few studies have examined this association. This study investigated cognitive and social performance of 30 subjects (CCDI x controls); CCDI were abstinent for 2 weeks. We used the Social Adjustment Scale (SAS), Wisconsin Card Sorting Test (WCST), and Iowa Gambling Task (IGT). Disadvantageous choices on the IGT were associated with higher levels of social dysfunction in CCDI, suggesting the ecological validity of the IGT. Social dysfunction and decision making may be linked to the same underlying prefrontal dysfunction, but the nature of this association should be further investigated. (Am J Addict 2010;00: 1-9).

A decision support system to facilitate management of patients with acute gastrointestinal bleeding

Objective: To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce health-care resources to those who need it the most. Design and methods: Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all. available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves. Results: Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model Conclusion: While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation. (c) 2007 Elsevier B.V. All rights reserved.