Effectiveness of hospital-based smoking cessation

Study objectives: Smoking cessation for current smokers is a health-care imperative. It is not clear which approaches to smoking cessation are the most effective in the hospital setting and which factors predict long-term abstinence. We hypothesized that a hospital-based smoking cessation program involving behavioral modification and support would provide an effective intervention for smoking cessation. Design: Prospective cohort study. Setting: Smoking cessation clinics in a tertiary referral, cardiothoracic hospital. Patients or participants: Two hundred forty-three smokers and 187 never-smoker control subjects. Interventions: Smokers underwent specific sessions of individual counseling on behavioral modification, including written information, advice about quit aids, and support during the quit attempt. Abstinence was confirmed by exhaled carbon monoxide measurements. Measurements and results: Compared to never-smoker control subjects, smokers were more likely to have grown up with a smoking father or siblings, and to currently live or socialize with other smokers. Two hundred sixteen smokers attended at least two sessions of the smoking cessation program. Of these, 25% were unavailable for follow-up at 12 months and were assumed to be smoking. The point prevalence abstinence rate at 12 months was 32%. Independent factors associated with abstinence at 12 months were self-belief in quitting ability, having a heart condition, growing up without siblings who smoked, and increasing number of pack-years. Conclusions: This prospective study has demonstrated that this hospital-based smoking cessation program was as effective as programs in other settings. Social and psychological factors were associated with a greater chance of abstinence.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

Cinderella no longer: alpha-catenin steps out of cadherin's shadow

To date, alpha-catenin has been best understood as an important cytoplasmic component of the classical cadherin complex responsible for cell-cell adhesion. By virtue of its capacity to bind F-actin, alpha-catenin was commonly envisaged to support cadherin function by coupling the adhesion receptor to the actin cytoskeleton. But is alpha-catenin solely the cadherin's handmaiden? A range of recent developments suggest, instead, that its biological activity is much more complex than previously appreciated. Evidence from cellular systems and model organisms demonstrates a clear, often dramatic, role for alpha-catenin in tissue organization and morphogenesis. The morphogenetic impact of alpha-catenin reflects its capacity to mediate functional cooperation between cadherins and the actin cytoskeleton, but is not confined to this. alpha-Catenin has a role in regulating cell proliferation and cadherin-independent pools of alpha-catenin may contribute to its functional impact.

Public reporting of hospital outcomes based on administrative data: risks and opportunities

In the wake of findings from the Bundaberg Hospital and Forster inquiries in Queensland, periodic public release of hospital performance reports has been recommended. A process for developing and releasing such reports is being established by Queensland Health, overseen by an independent expert panel. This recommendation presupposes that public reports based on routinely collected administrative data are accurate; that the public can access, correctly interpret and act upon report contents; that reports motivate hospital clinicians and managers to improve quality of care; and that there are no unintended adverse effects of public reporting. Available research suggests that primary data sources are often inaccurate and incomplete, that reports have low predictive value in detecting outlier hospitals, and that users experience difficulty in accessing and interpreting reports and tend to distrust their findings.

Cognitive neuroimaging: Cognitive science out of the armchair

Cognitive scientists were not quick to embrace the functional neuroimaging technologies that emerged during the late 20th century. In this new century, cognitive scientists continue to question, not unreasonably, the relevance of functional neuroimaging investigations that fail to address questions of interest to cognitive science. However, some ultra-cognitive scientists assert that these experiments can never be of relevance to the Study of cognition. Their reasoning reflects an adherence to a functionalist philosophy that arbitrarily and purposefully distinguishes mental information-processing systems from brain or brain-like operations. This article addresses whether data from properly conducted functional neuroimaging studies can inform and Subsequently constrain the assumptions of theoretical cognitive models. The article commences with a focus upon the functionalist philosophy espoused by the ultra-cognitive scientists, contrasting it with the materialist philosophy that motivates both cognitive neuromiaging investigations and connectionist modelling of cognitive systems. Connectionism and cognitive neuroimaging share many features, including an emphasis on unified cognitive and neural models of systems that combine localist and distributed representations. The utility of designing cognitive neuroimaging studies to test (primarily) connectionist models of cognitive phenomena is illustrated using data from functional magnetic resonance imaging (fMRI) investigations of language production and episodic memory. (C) 2005 Elsevier Inc. All rights reserved.