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PubMed2002

The hidden curriculum in multicultural medical education: the role of case examples.

Explicit cross-cultural learning experiences in medical education are provided within the context of implicit experiences provided by a greater "hidden curriculum." The authors conducted a content analysis of 983 cases presented in the 1996-1998 year one and year two curriculum at the University of Minnesota Medical School to determine in what ways they might embody elements of the hidden curriculum, i.e., how they either supported or undermined explicit messages about diverse patient populations. Cases were coded for demographic variables, potential risk factors, and diagnoses or presenting problems. The findings revealed that cases featuring males out-numbered those featuring females; this ratio differed across courses, and appeared to differ from the actual epidemiology of the conditions. Sexual orientation was specified infrequently. When sexual orientation and behavior were specified, these appeared in the context of a risk assessment for particular diseases (e.g., HIV infection). Most cases did not provide racial or ethnic descriptions. For many of the ethnic descriptors, links to genetic, cultural, or socioeconomic factors were apparent; no such link was apparent when the racial terms "white" or "Caucasian" were used. Analysis of the 983 cases shows that the pattern of demographics and associations of particular groups with diseases or risk factors in cases conveys messages, as does the lack of mention of sexual orientation and race or ethnicity. These messages are inconsistent with and may undermine the formal multicultural medical curriculum. The results suggest a need for formal deliberation of this aspect of the curriculum by curriculum planners.

Cultural Diversity↗
PubMed1979

The regularity of muscle spindle discharge in man.

1. The variability of discharge of thirty-nine muscle spindle afferents from the pretibial muscles of normal human subjects was determined for spike train sequences recorded with the ankle joint fixed in 25 degrees plantar flexion, during further stretch and during graded voluntary contractions of the receptor-bearing muscle. 2. In non-contracting muscles with the ankle joint in 25 degrees plantar flexion, a sustained discharge was maintained by twenty-four of the thirty-nine endings. The mean discharge frequency for the active endings was 11.1 Hz (range 4.8--22.1 Hz), the mean coefficient of variation 0.073 (range 0.021--0.183). With further stretch, the discharge of endings maintaining frequencies below 10--12 Hz became more regular. For endings maintaining higher frequencies, changes in the coefficient of variation were small and occurred in either direction. All secondary endings maintained a highly regular discharge, but, at these frequencies, there was no statistically significant difference in the variability of primary and secondary endings. 3. It is considered that these findings are comparable to those of Matthews & Stein (1969) for de-efferented feline spindle endings, and support the view that there is no functionally effective background fusimotor drive to non-contracting muscles of normal human subjects. 4. A voluntary contraction sufficient to accelerate a spindle ending invariably decreased the regularity of its afferent discharge. During voluntary contractions, coefficients of variation up to 0.345 were recorded. However, coefficients as low as 0.1 were not uncommon, and thus the absence of fusimotor drive cannot necessarily be inferred from a regular afferent discharge pattern. 5. With contractions of different strength, the increase in the coefficient of variation did not parallel the increase in discharge frequency. It is concluded that not all fusimotor influences acting on a spindle ending are translated into variability, and that measurements of the variability of discharge do not accurately reflect the level of fusimotor drive. 6. The discharge frequency of some spindle endings decreased slightly in some contractions and this was accompanied by an increase in the variability of discharge. It is suggested that contracting extrafusal muscle fibres can modulate the discharge pattern of spindle endings and contribute to the variability of discharge during a voluntary contraction. 7. In contracting muscles the irregular fusimotor-driven spindle discharge contained a 'hidden' periodicity, but this was not as extensive as has been reported for the cat. No such periodicity could be demonstrated for spindle endings in non-contracting human muscles.

Action Potentials↗
PubMed1993

Nonimmune macromolecular complexes of Ig in human gut lumen. Probable enhancement of antibody functions.

Protein Fv, a human sialoprotein recently described in the stools of patients suffering from liver diseases, binds the variable domain of H chains without impairing Ag binding. Normal subjects are shown here to secrete protein Fv under a hidden form, saturated with luminal Ig. In feces, the nonimmune complexes are essentially of 1800 and 800 kDa M(r); they contain protein Fv molecules bound with F(ab')2 fragments produced by cleavage of Secretory IgA during colonic transit. The 800-kDa complexes correspond to 6 molecules of F(ab')2 fragments bound to a sole protein Fv dimer. This was established by comparison with an in vitro-made complex having a valency of 6. Investigation of the role of protein Fv shows that in vitro addition of this free molecule to antivirus or to anti-Salmonella typhi antibodies allows or augments agglutination of the corresponding pathogens. This property is of major interest for secretory antibodies because it favors their role in Ag conveyance in the mucus stream. It seems therefore that protein Fv is a novel key factor of the immune defense in gut.

Agglutination↗
PubMed2002

The sliding window correlation procedure for detecting hidden correlations: existence of behavioral subgroups illustrated with aged rats.

We developed the sliding window correlation procedure in order to examine populations for possible heterogeneity in the ways two variables are related with each other. This procedure involves computing correlation coefficients (R) for overlapping successive segments of the covariate scores. The distribution of resulting Rs reveals fluctuations in the degree and direction of R over the sample of ranked scores. This procedure is applied to behavioral data of aged rats, which were rank-ordered according to water maze performance, and correlated with open field exploration and conflict behavior in a light/dark chamber. Results revealed correlation coefficients of varying magnitudes and opposing directions for different segments of the population, which were obscured by overall correlation analysis. E.g. for the superior learners, the Rs were highest between maze learning ability, increased open field exploration and reduced anxiety in the conflict test, whereas for the intermediate learners the Rs were highest for maze learning ability related with reduced exploration and increased anxiety. Thus, the sliding window correlation distribution can be applied in conjunction with overall correlation analysis to provide information about the potential presence and locations of subgroups within a population, especially if overall correlation analysis does not yield significant results.

Aging↗
PubMed2006

Hidden Markov multiple event sequence models: A paradigm for the spatio-temporal analysis of fMRI data.

This paper presents a novel, completely unsupervised fMRI brain mapping method that addresses the three problems of hemodynamic response function (HRF) variability, hemodynamic event timing, and fMRI response non-linearity. Spatial and temporal information are directly taken into account into the core of the activation detection process. In practice, activation detection at voxel v is formulated in terms of temporal alignment between sequences of hemodynamic response onsets (HROs) detected in the fMRI signal at v and in the spatial neighborhood of v, and the input sequence of stimuli or stimulus onsets. Event-related and epoch paradigms are considered. The multiple event sequence alignment problem is solved within the probabilistic framework of hidden Markov multiple event sequence models (HMMESMs), a new class of hidden Markov models. Results obtained on real and synthetic data significantly outperform those obtained with the popular statistical parametric mapping (SPM2) method without requiring any prior definition of the expected activation patterns, the HMMESM mapping approach being completely unsupervised.

Adolescent↗
PubMed2003

Prediction of survival in surgical unresectable lung cancer by artificial neural networks including genetic polymorphisms and clinical parameters.

Lung cancer, a common malignancy in Taiwan, involves multiple factors, including genetics and environmental factors. The survival time is very short once cancer is diagnosed as being in advanced stage and surgically unresectable. Therefore, a good model of prediction of disease outcome is important for a treatment plan. We investigated the survival time in advanced lung cancer by using computer science from the genetic polymorphism of the p21 and p53 genes in conjunction with patients' general data. We studied 75 advanced and surgical unresectable lung cancer patients. The prediction of survival time was made by comparing real data obtained from follow-up periods with data generated by an artificial neural network (ANN). The most important input variable was the clinical staging of lung cancer patients. The second and third most important variables were pathological type and responsiveness to treatment, respectively. There were 25 neurons in the input layer, four neurons in the hidden layer-1, and one neuron in the output layer. The predicted accuracy was 86.2%. The average survival time was 12.44 +/- 7.95 months according to real data and 13.16 +/- 1.77 months based on the ANN results. ANN provides good prediction results when clinical parameters and genetic polymorphisms are considered in the model. It is possible to use computer science to integrate the genetic polymorphisms and clinical parameters in the prediction of disease outcome. Data mining provides a promising approach to the study of genetic markers for advanced lung cancer.

Adult↗
PubMed2000

Group impressions as dynamic configurations: the tensor product model of group impression formation and change.

Group impressions are dynamic configurations. The tensor product model (TPM), a connectionist model of memory and learning, is used to describe the process of group impression formation and change, emphasizing the structured and contextualized nature of group impressions and the dynamic evolution of group impressions over time. TPM is first shown to be consistent with algebraic models of social judgment (the weighted averaging model; N. Anderson, 1981) and exemplar-based social category learning (the context model; E. R. Smith & M. A. Zárate, 1992), providing a theoretical reduction of the algebraic models to the present connectionist framework. TPM is then shown to describe a common process that underlies both formation and change of group impressions despite the often-made assumption that they constitute different psychological processes. In particular, various time-dependent properties of both group impression formation (e.g., time variability, response dependency, and order effects in impression judgments) and change (e.g., stereotype change and group accentuation) are explained, demonstrating a hidden unity beneath the diverse array of empirical findings. Implications of the model for conceptualizing stereotype formation and change are discussed.

Attitude↗
PubMed2005

A flexible coefficient smooth transition time series model.

In this paper, we consider a flexible smooth transition autoregressive (STAR) model with multiple regimes and multiple transition variables. This formulation can be interpreted as a time varying linear model where the coefficients are the outputs of a single hidden layer feedforward neural network. This proposal has the major advantage of nesting several nonlinear models, such as, the self-exciting threshold autoregressive (SETAR), the autoregressive neural network (AR-NN), and the logistic STAR models. Furthermore, if the neural network is interpreted as a nonparametric universal approximation to any Borel measurable function, our formulation is directly comparable to the functional coefficient autoregressive (FAR) and the single-index coefficient regression models. A model building procedure is developed based on statistical inference arguments. A Monte Carlo experiment showed that the procedure works in small samples, and its performance improves, as it should, in medium size samples. Several real examples are also addressed.

Algorithms↗
PubMed2005

Comparison between logistic regression and neural networks to predict death in patients with suspected sepsis in the emergency room.

INTRODUCTION: Neural networks are new methodological tools based on nonlinear models. They appear to be better at prediction and classification in biological systems than do traditional strategies such as logistic regression. This paper provides a practical example that contrasts both approaches within the setting of suspected sepsis in the emergency room. METHODS: The study population comprised patients with suspected bacterial infection as their main diagnosis for admission to the emergency room at two University-based hospitals. Mortality within the first 28 days from admission was predicted using logistic regression with the following variables: age, immunosuppressive systemic disease, general systemic disease, Shock Index, temperature, respiratory rate, Glasgow Coma Scale score, leucocyte counts, platelet counts and creatinine. Also, with the same input and output variables, a probabilistic neural network was trained with an adaptive genetic algorithm. The network had three neurone layers: 10 neurones in the input layer, 368 in the hidden layer and two in the output layer. Calibration was measured using the Hosmer-Lemeshow goodness-of-fit test and discrimination was determined using receiver operating characteristic curves. RESULTS: A total of 533 patients were recruited and overall 28-day mortality was 19%. The factors chosen by logistic regression (with their score in parentheses) were as follows: immunosuppressive systemic disease or general systemic disease (2), respiratory rate 24-33 breaths/min (1), respiratory rate > or = 34 breaths/min (3), Glasgow Come Scale score < or = 12 (3), Shock Index > or = 1.5 (2) and temperature < 38 degrees C (2). The network included all variables and there were no significant differences in predictive ability between the approaches. The areas under the receiver operating characteristic curves were 0.7517 and 0.8782 for the logistic model and the neural network, respectively (P = 0.037). CONCLUSION: A predictive model would be an extremely useful tool in the setting of suspected sepsis in the emergency room. It could serve both as a guideline in medical decision-making and as a simple way to select or stratify patients in clinical research. Our proposed model and the specific development method -- either logistic regression or neural networks -- must be evaluated and validated in an independent population.

Adolescent↗
PubMed2003

Matrix decomposition model for investigating prehistoric intracemetery biological variation.

A matrix decomposition model for analyzing prehistoric intracemetery biological variability is presented. The model, based on the correlation between interindividual biological and burial distance matrices, provides an estimate of the number of distinct burial populations interred within a single cemetery, which effectively ameliorates identifiability problems typical of general variability analyses (Raemsch [1995] Ph.D. dissertation, SUNY at Albany). Unlike other methods for analyzing intrasite variability analysis (e.g., kinship analysis and microchronology), this method is not reliant on a priori subgroup definition. Assuming a kin-structured burial pattern, a residual matrix is defined based on the raw data matrices; the proportion of negative or zero residuals to the total number of residuals provides an estimate of the number of hidden subgroupings (lineages) within the cemetery. The comparative utility of the model is demonstrated on a series of protohistoric and historic period skeletal samples from the Georgia coast, where it demonstrated that the increase in phenotypic variability during the early mission period is the result of population aggregation at the missions and not necessarily genetic admixture between local and nonlocal groups.

Anthropology, Physical↗
PubMed2006

Not the same everywhere. Patient-centered learning environments at nine medical schools.

BACKGROUND: Learning environments overtly or implicitly address patient-centered values and have been the focus of research for more than 40 years, often in studies about the "hidden curriculum." However, many of these studies occurred at single medical schools and used time-intensive ethnographic methods. This field of inquiry lacks survey methods and information about how learning environments differ across medical schools. OBJECTIVE: To examine patient-centered characteristics of learning environments at 9 U.S. medical schools. DESIGN: Cross-sectional internet-based survey. PARTICIPANTS: Eight-hundred and twenty-three third- and fourth-year medical students in the classes of 2002 and 2003. MEASUREMENTS: We measured the patient-centeredness of learning environments with the Communication, Curriculum, and Culture (C3) Instrument, a 29-item validated measure that characterizes the degree to which a medical school's environment fosters patient-centered care. The C3 Instrument contains 3 content areas (role modeling, students' experiences, and support for students' patient-centered behaviors), and is designed to measure these areas independent of respondents' attitudes about patient-centered care. We also collected demographic and attitudinal information from respondents. RESULTS: The variability of C3 scores across schools in each of the 3 content areas of the instrument was striking and statistically significant (P values ranged from .001 to .004). In addition, the patterns of scores on the 3 content areas differed from school to school. CONCLUSIONS: The 9 schools demonstrated unique and different learning environments both in terms of magnitude and patterns of characteristics. Further multiinstitutional study of hidden curricula is needed to further establish the degree of variability that exists, and to assist educators in making informed choices about how to intervene at their own schools.

Adult↗
PubMed1987

Hidden bias in the use of archival data.

Nonresponses in archival data may violate the missing-at-random assumption in ways difficult to detect. Standard methods of comparing sociodemographics of respondents and nonrespondents are inappropriate when the units of analysis are not also the individuals who maintain the archival record. Under these circumstances, the distribution of missing data may be correlated with the dependent variable and traits of the record keepers. This will distort relationships, especially when listwise deletion of missing values is used in multivariate analysis. Data are used from a large clinical chart study of mentally ill patients to demonstrate the process of identifying hidden bias and the implications of such bias.

Archives↗
PubMed1991

Home environmental consequences of commute travel impedance.

The physical and perceptual dimensions of commuting travel impedance were again found to have stressful consequences in a study of 99 employees of two companies. This quasi-experimental replication study, which focuses here on home environment consequences, investigated the effects of physical impedance and subjective impedance on multivariate measures of residential satisfaction and personal affect in the home. Both sets of residential outcome measures were significantly related to the two impedance dimensions. As predicted, gender was a significant moderator of physical impedance effects. Women commuting on high physical impedance routes were most negatively affected. Previously found subjective impedance effects on negative home mood, regardless of gender, were strongly replicated with several methods and were buttressed by convergent results with objective indices. The theoretical conjecture that subjective impedance mediates the stress effects of physical impedance was supported by the personal affect cluster but only for one variable in the residential satisfaction cluster. Traffic congestion has increased in metropolitan areas nationwide, and commuters, families, and organizations are absorbing associated hidden costs. The results are reviewed in terms of our ecological model, and the moderating effects of gender are discussed in terms of choice and role constraints.

Adult↗
PubMed1990

Weight control and appetite--a genetic perspective.

It is proposed that weight control and appetite are more strongly inherited than currently acknowledged. This view, based on evidence from a wide range of animal species, applies to the normal range of variability and to the more extreme eating disorders of obesity and anorexia. Diet choice and activity are seen as triggering factors uncovering hidden layers of the genome. If the hypothesis is accepted it suggests several modifications to our current understanding of eating disorders and indicates changes in methods of prevention and control in practice.

Journal Article↗
PubMed1997

Coronary artery bypass risk prediction using neural networks.

BACKGROUND: Neural networks are nonparametric, robust, pattern recognition techniques that can be used to model complex relationships. METHODS: The applicability of multilayer perceptron neural networks (MLP) to coronary artery bypass grafting risk prediction was assessed using The Society of Thoracic Surgeons database of 80,606 patients who underwent coronary artery bypass grafting in 1993. The results of traditional logistic regression and Bayesian analysis were compared with single-layer (no hidden layer), two-layer (one hidden layer), and three-layer (two hidden layer) MLP neural networks. These networks were trained using stochastic gradient descent with early stopping. All prediction models used the same variables and were evaluated by training on 40,480 patients and cross-validation testing on a separate group of 40,126 patients. Techniques were also developed to calculate effective odds ratios for MLP networks and to generate confidence intervals for MLP risk predictions using an auxiliary "confidence MLP." RESULTS: Receiver operating characteristic curve areas for predicting mortality were approximately 76% for all classifiers, including neural networks. Calibration (accuracy of posterior probability prediction) was slightly better with a two-member committee classifier that averaged the outputs of a MLP network and a logistic regression model. Unlike the individual methods, the committee classifier did not overestimate or underestimate risk for high-risk patients. CONCLUSIONS: A committee classifier combining the best neural network and logistic regression provided the best model calibration, but the receiver operating characteristic curve area was only 76% irrespective of which predictive model was used.

Bayes Theorem↗
PubMed1997

Neural networks as 'software sensors' in enzyme production.

Industrial applications of enzyme technology are rapidly increasing. On-line control of enzyme production processes, however, is difficult, owing to the uncertainties typical of biological reactions and to the lack of suitable sensors. We demonstrate that well-trained feedforward backpropagation neural networks with one hidden layer can be employed to overcome such problems with no need for a priori knowledge of the relationships of the process variables involved. Neural network programs were written in Microsoft Visual C+2 for Windows and implemented in a personal computer. The goodness of fit of the trained neural network to the reference data was determined by the coefficient of determination R2. On-line state estimation and multi-step ahead prediction of enzyme activity and biomass concentration, both in a yeast lipase and fungal glucoamylase production could be satisfactorily carried out. Results showed an excellent fit for estimated lipase activity (R2 = 0.988) and biomass concentration (R2 = 0.989). In glucoamylase production, both enzyme activity and biomass concentration could also be reliably predicted for 2 time intervals (10 h) ahead with only on-line measurable parameter values as the input data.

Algorithms↗
PubMed2005

A novel device for the measurement of proprioception in the hand.

BACKGROUND: Together with visual information, awareness of limb position and movement is essential for limb coordination. A proprioceptive deficit has been demonstrated in a number of rheumatological disorders. There is a lack of a portable device for measuring hand proprioception in the field. METHODS: A compact portable device for measuring joint position sense in the metacarpophalangeal joint of the index finger of either hand was constructed. This device was manually operated and required the subject to match the position of the hidden finger with a surface-mounted silhouette. Reliability studies were performed over three consecutive days in 12 normal volunteers. RESULTS: Intrasubject variability [95% confidence interval (CI)] was 0.86 degrees (0.04-1.76) between days 1 and 2, and 1.23 degrees (1.04-3.50) between days 2 and 3. The intraclass correlation coefficient (95% CI) between all 3 days was 0.92 (0.85-0.96). Average proprioceptive error (95% CI) in the sample population was 5.72 degrees (1.23-10.2) over the 3 days. This value was 5.94 degrees , 5.79 degrees and 5.42 degrees on days 1, 2 and 3, respectively. No difference was found between sexes but dominant hands gave smaller errors (mean dominant error 5.11 degrees , mean non-dominant error 6.35 degrees ; t = -3.4, P = 0.002). CONCLUSIONS: This report describes a new portable device for measuring proprioception in the hand. Reproducibility was shown to be good on an individual and group basis. These results are promising and warrant larger age- and sex-related studies. The ease and portability of the device make it ideal for use in epidemiological studies of rheumatological disorders involving the hands, including joint hypermobility.

Adult↗
PubMed2003

Classification of neck movement patterns related to whiplash-associated disorders using neural networks.

This paper presents a new method for classification of neck movement patterns related to Whiplash-associated disorders (WAD) using a resilient backpropagation neural network (BPNN). WAD are a common diagnosis after neck trauma, typically caused by rear-end car accidents. Since physical injuries seldom are found with present imaging techniques, the diagnosis can be difficult to make. The active range of the neck is often visually inspected in patients with neck pain, but this is a subjective measure, and a more objective decision support system, that gives a reliable and more detailed analysis of neck movement pattern, is needed. The objective of this study was to evaluate the predictive ability of a BPNN, using neck movement variables as input. Three-dimensional (3-D) neck movement data from 59 subjects with WAD and 56 control subjects were collected with a ProReflex system. Rotation angle and angle velocity were calculated using the instantaneous helical axis method and motion variables were extracted. A principal component analysis was performed in order to reduce data and improve the BPNN performance. BPNNs with six hidden nodes had a predictivity of 0.89, a sensitivity of 0.90 and a specificity of 0.88, which are very promising results. This shows that neck movement analysis combined with a neural network could build the basis of a decision support system for classifying suspected WAD, even though further evaluation of the method is needed.

Adult↗