Counsellor behaviours and patient language during brief motivational interventions: a sequential analysis of speech

AIMS: To investigate empirically the hypothesized relationship between counsellor motivational interviewing (MI) skills and patient change talk (CT) by analysing the articulation between counsellor behaviours and patient language during brief motivational interventions (BMI) addressing at-risk alcohol consumption. DESIGN: Sequential analysis of psycholinguistic codes obtained by two independent raters using the Motivational Interviewing Skill Code (MISC), version 2.0. SETTING: Secondary analysis of data from a randomized controlled trial evaluating the effectiveness of BMI in an emergency department. PARTICIPANTS: A total of 97 patients tape-recorded when receiving BMI. MEASUREMENTS: MISC variables were categorized into three counsellor behaviours (MI-consistent, MI-inconsistent and 'other') and three kinds of patient language (CT, counter-CT (CCT) and utterances not linked with the alcohol topic). Observed transition frequencies, conditional probabilities and significance levels based on odds ratios were computed using sequential analysis software. FINDINGS: MI-consistent behaviours were the only counsellor behaviours that were significantly more likely to be followed by patient CT. Those behaviours were significantly more likely to be followed by patient change exploration (CT and CCT) while MI-inconsistent behaviours and 'other' counsellor behaviours were significantly more likely to be followed by utterances not linked with the alcohol topic and significantly less likely to be followed by CT. MI-consistent behaviours were more likely after change exploration, whereas 'other' counsellor behaviours were more likely only after utterances not linked with the alcohol topic. CONCLUSIONS: Findings lend support to the hypothesized relationship between MI-consistent behaviours and CT, highlight the importance of patient influence on counsellor behaviour and emphasize the usefulness of MI techniques and spirit during brief interventions targeting change enhancement.

Obesity and survival in operable breast cancer patients treated with adjuvant anthracyclines and taxanes according to pathological subtypes: a pooled analysis.

INTRODUCTION Obesity is an unfavorable prognostic factor in breast cancer (BC) patients regardless of menopausal status and treatment received. However, the association between obesity and survival outcome by pathological subtype requires further clarification. METHODS We performed a retrospective analysis including 5,683 operable BC patients enrolled in four randomized clinical trials (GEICAM/9906, GEICAM/9805, GEICAM/2003-02, and BCIRG 001) evaluating anthracyclines and taxanes as adjuvant treatments. Our primary aim was to assess the prognostic effect of body mass index (BMI) on disease recurrence, breast cancer mortality (BCM), and overall mortality (OM). A secondary aim was to detect differences of such prognostic effects by subtype. RESULTS Multivariate survival analyses adjusting for age, tumor size, nodal status, menopausal status, surgery type, histological grade, hormone receptor status, human epidermal growth factor receptor 2 (HER2) status, chemotherapy regimen, and under-treatment showed that obese patients (BMI 30.0 to 34.9) had similar prognoses to that of patients with a BMI < 25 (reference group) in terms of recurrence (Hazard Ratio [HR] = 1.08, 95% Confidence Interval [CI] = 0.90 to 1.30), BCM (HR = 1.02, 0.81 to 1.29), and OM (HR = 0.97, 0.78 to 1.19). Patients with severe obesity (BMI ≥ 35) had a significantly increased risk of recurrence (HR = 1.26, 1.00 to 1.59, P = 0.048), BCM (HR = 1.32, 1.00 to 1.74, P = 0.050), and OM (HR = 1.35, 1.06 to 1.71, P = 0.016) compared to our reference group. The prognostic effect of severe obesity did not vary by subtype. CONCLUSIONS Severely obese patients treated with anthracyclines and taxanes present a worse prognosis regarding recurrence, BCM, and OM than patients with BMI < 25. The magnitude of the harmful effect of BMI on survival-related outcomes was similar across subtypes.

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis.

Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analysis and interpretation. We present a method, which allows automatic detection of epileptiform events and discrimination of them from eye blinks, and is based on features derived using a novel application of independent component analysis. The algorithm was trained and cross validated using seven EEGs with epileptiform activity. For epileptiform events with compensation for eyeblinks, the sensitivity was 65 +/- 22% at a specificity of 86 +/- 7% (mean +/- SD). With feature extraction by PCA or classification of raw data, specificity reduced to 76 and 74%, respectively, for the same sensitivity. On exactly the same data, the commercially available software Reveal had a maximum sensitivity of 30% and concurrent specificity of 77%. Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG.

Signal search analysis server.

Signal search analysis is a general method to discover and characterize sequence motifs that are positionally correlated with a functional site (e.g. a transcription or translation start site). The method has played an instrumental role in the analysis of eukaryotic promoter elements. The signal search analysis server provides access to four different computer programs as well as to a large number of precompiled functional site collections. The programs offered allow: (i) the identification of non-random sequence regions under evolutionary constraint; (ii) the detection of consensus sequence-based motifs that are over- or under-represented at a particular distance from a functional site; (iii) the analysis of the positional distribution of a consensus sequence- or weight matrix-based sequence motif around a functional site; and (iv) the optimization of a weight matrix description of a locally over-represented sequence motif. These programs can be accessed at: http://www.isrec.isb-sib.ch/ssa/.

Comparative genomics of the vertebrate insulin/TOR signal transduction pathway: A network-level analysis of selective pressures

Complexity of biological function relies on large networks of interacting molecules. However, the evolutionary properties of these networks are not fully understood. It has been shown that selective pressures depend on the position of genes in the network. We have previously shown that in the Drosophila insulin/target of rapamycin (TOR) signal transduction pathway there is a correlation between the pathway position and the strength of purifying selection, with the downstream genes being most constrained. In this study, we investigated the evolutionary dynamics of this well-characterized pathway in vertebrates. More specifically, we determined the impact of natural selection on the evolution of 72 genes of this pathway. We found that in vertebrates there is a similar gradient of selective constraint in the insulin/TOR pathway to that found in Drosophila. This feature is neither the result of a polarity in the impact of positive selection nor of a series of factors affecting selective constraint levels (gene expression level and breadth, codon bias, protein length, and connectivity). We also found that pathway genes encoding physically interacting proteins tend to evolve under similar selective constraints. The results indicate that the architecture of the vertebrate insulin/TOR pathway constrains the molecular evolution of its components. Therefore, the polarity detected in Drosophila is neither specific nor incidental of this genus. Hence, although the underlying biological mechanisms remain unclear, these may be similar in both vertebrates and Drosophila.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

A Fast Gradient Approximation for Nonlinear Blind Signal Processing

When dealing with nonlinear blind processing algorithms (deconvolution or post-nonlinear source separation), complex mathematical estimations must be done giving as a result very slow algorithms. This is the case, for example, in speech processing, spike signals deconvolution or microarray data analysis. In this paper, we propose a simple method to reduce computational time for the inversion of Wiener systems or the separation of post-nonlinear mixtures, by using a linear approximation in a minimum mutual information algorithm. Simulation results demonstrate that linear spline interpolation is fast and accurate, obtaining very good results (similar to those obtained without approximation) while computational time is dramatically decreased. On the other hand, cubic spline interpolation also obtains similar good results, but due to its intrinsic complexity, the global algorithm is much more slow and hence not useful for our purpose.

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

Reconciling phonological neighborhood effects in speech production through single trial analysis

A crucial step for understanding how lexical knowledge is represented is to describe the relative similarity of lexical items, and how it influences language processing. Previous studies of the effects of form similarity on word production have reported conflicting results, notably within and across languages. The aim of the present study was to clarify this empirical issue to provide specific constraints for theoretical models of language production. We investigated the role of phonological neighborhood density in a large-scale picture naming experiment using fine-grained statistical models. The results showed that increasing phonological neighborhood density has a detrimental effect on naming latencies, and re-analyses of independently obtained data sets provide supplementary evidence for this effect. Finally, we reviewed a large body of evidence concerning phonological neighborhood density effects in word production, and discussed the occurrence of facilitatory and inhibitory effects in accuracy measures. The overall pattern shows that phonological neighborhood generates two opposite forces, one facilitatory and one inhibitory. In cases where speech production is disrupted (e.g. certain aphasic symptoms), the facilitatory component may emerge, but inhibitory processes dominate in efficient naming by healthy speakers. These findings are difficult to accommodate in terms of monitoring processes, but can be explained within interactive activation accounts combining phonological facilitation and lexical competition.

Alzheimer Disease Diagnosis based on Automatic Spontaneous Speech Analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to improvement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

Clinico-Pathological discrepancies in the Diagnosis of Causes of Maternal Death in Sub-Saharan Africa: Retrospective Analysis

Background Maternal mortality is a major public-health problem in developing countries. Extreme differences in maternal mortality rates between developed and developing countries indicate that most of these deaths are preventable. Most information on the causes of maternal death in these areas is based on clinical records and verbal autopsies. Clinical diagnostic errors may play a significant role in this problem and might also have major implications for the evaluation of current estimations of causes of maternal death. Methods and Findings A retrospective analysis of clinico-pathologic correlation was carried out, using necropsy as the gold standard for diagnosis. All maternal autopsies (n ¼ 139) during the period from October 2002 to December 2004 at the Maputo Central Hospital, Mozambique were included and major diagnostic discrepancies were analyzed (i.e., those involving the cause of death). Major diagnostic errors were detected in 56 (40.3%) maternal deaths. A high rate of false negative diagnoses was observed for infectious diseases, which showed sensitivities under 50%: HIV/AIDS-related conditions (33.3%), pyogenic bronchopneumonia (35.3%), pyogenic meningitis (40.0%), and puerperal septicemia (50.0%). Eclampsia, was the main source of false positive diagnoses, showing a low predictive positive value (42.9%). Conclusions Clinico-pathological discrepancies may have a significant impact on maternal mortality in sub-Saharan Africa and question the validity of reports based on clinical data or verbal autopsies. Increasing clinical awareness of the impact of obstetric and nonobstetric infections with their inclusion in the differential diagnosis, together with a thorough evaluation of cases clinically thought to be eclampsia, could have a significant impact on the reduction of maternal mortality.

Clinico-Pathological discrepancies in the Diagnosis of Causes of Maternal Death in Sub-Saharan Africa: Retrospective Analysis

Background Maternal mortality is a major public-health problem in developing countries. Extreme differences in maternal mortality rates between developed and developing countries indicate that most of these deaths are preventable. Most information on the causes of maternal death in these areas is based on clinical records and verbal autopsies. Clinical diagnostic errors may play a significant role in this problem and might also have major implications for the evaluation of current estimations of causes of maternal death. Methods and Findings A retrospective analysis of clinico-pathologic correlation was carried out, using necropsy as the gold standard for diagnosis. All maternal autopsies (n ¼ 139) during the period from October 2002 to December 2004 at the Maputo Central Hospital, Mozambique were included and major diagnostic discrepancies were analyzed (i.e., those involving the cause of death). Major diagnostic errors were detected in 56 (40.3%) maternal deaths. A high rate of false negative diagnoses was observed for infectious diseases, which showed sensitivities under 50%: HIV/AIDS-related conditions (33.3%), pyogenic bronchopneumonia (35.3%), pyogenic meningitis (40.0%), and puerperal septicemia (50.0%). Eclampsia, was the main source of false positive diagnoses, showing a low predictive positive value (42.9%). Conclusions Clinico-pathological discrepancies may have a significant impact on maternal mortality in sub-Saharan Africa and question the validity of reports based on clinical data or verbal autopsies. Increasing clinical awareness of the impact of obstetric and nonobstetric infections with their inclusion in the differential diagnosis, together with a thorough evaluation of cases clinically thought to be eclampsia, could have a significant impact on the reduction of maternal mortality.