Getting some space: Infants' and caregivers' containment and support spatial constructions during play.

Using a cross-sectional design, we examined the containment and support spatial constructions infants spontaneously create and those they observe when playing with a nesting toy. Infants (N=76) of 8, 13, or 18months played alone for 2min and then played with a caregiver for another 2min. At 8months, infants created few relations; at 13months, they inserted objects, resulting in containment, and stacked objects, resulting in support; at 18months, they created more than three times more containment relations than support relations, a result replicated in a second study. In contrast, caregivers created more support relations than containment relations, regardless of infant age, but labeled containment more than support. The results highlight differential exposure to containment and support in infant solitary and dyadic play. By 18months, infants gain greater firsthand experience with containment, a relation that is further reinforced by caregiver labeling.

Infant sensitivity to speaker and language in learning a second label.

Two experiments examined when monolingual, English-learning 19-month-old infants learn a second object label. Two experimenters sat together. One labeled a novel object with one novel label, whereas the other labeled the same object with a different label in either the same or a different language. Infants were tested on their comprehension of each label immediately following its presentation. Infants mapped the first label at above chance levels, but they did so with the second label only when requested by the speaker who provided it (Experiment 1) or when the second experimenter labeled the object in a different language (Experiment 2). These results show that 19-month-olds learn second object labels but do not readily generalize them across speakers of the same language. The results highlight how speaker and language spoken guide infants' acceptance of second labels, supporting sociopragmatic views of word learning.

Learning to form a spatial category of tight-fit relations: how experience with a label can give a boost.

Two experiments explored the ability of 18-month-old infants to form an abstract categorical representation of tight-fit spatial relations in a visual habituation task. In Experiment 1, infants formed an abstract spatial category when hearing a familiar word (tight) during habituation but not when viewing the events in silence or when hearing a novel word. In Experiment 2, infants were given experience viewing and producing tight-fit relations while an experimenter labeled them with a novel word. Following this experience, infants formed the tight-fit spatial category in the visual habituation task, particularly when hearing the novel word again during habituation. Results suggest that even brief experience with a label and tight-fit relations can aid infants in forming an abstract categorical representation of tight-fit relations.

Do novel words facilitate 18-month-olds' spatial categorization?

Eighteen-month-olds' spatial categorization was tested when hearing a novel spatial word. Infants formed an abstract categorical representation of support (i.e., placing 1 object on another) when hearing a novel spatial particle during habituation but not when viewing the events in silence. Infants with a productive spatial vocabulary did not discriminate the support relation when hearing the same novel word as a count noun. However, infants who were not yet producing spatial words did attend to the support relation when presented with the novel count noun. The results indicate that 18-month-olds can use a novel particle (possibly assisted by a familiar verb) to facilitate their spatial categorization but that the specificity of this effect varies with infants' acquisition of spatial language.

Vitamin A supplementation reduces the monocyte chemoattractant protein-1 intestinal immune response of Mexican children.

The impact of vitamin A supplementation on childhood diarrhea may be determined by the regulatory effect supplementation has on the mucosal immune response in the gut. Previous studies have not addressed the impact of vitamin A supplementation on the production of monocyte chemoattractant protein 1 (MCP-1), an essential chemokine involved in pathogen-specific mucosal immune response. Fecal MCP-1 concentrations, determined by an enzyme-linked immuno absorption assay, were compared among 127 Mexican children 5-15 mo of age randomized to receive a vitamin A supplement (<12 mo of age, 20,000 IU of retinol; > or =12 mo, 45,000 iu) every 2 mo or a placebo as part of a larger vitamin A supplementation trial. Stools collected during the summer months were screened for MCP-1 and gastrointestinal pathogens. Values of MCP-1 were categorized into 3 levels (nondetectable, or =median). Multinomial logistic regression models were used to determine whether vitamin A-supplemented children had different categorical values of MCP-1 compared with children in the placebo group. Differences in categorical values were also analyzed stratified by gastrointestinal pathogen infections and by diarrheal symptoms. Overall, children who received the vitamin A supplement had reduced fecal concentrations of MCP-1 compared with children in the placebo group (median pg/mg protein +/- interquartile range: 284.88 +/- 885.35 vs. 403.39 +/- 913.16; odds ratio 0.64, 95% CI 0.42-97, P = 0.03). Vitamin A supplemented children infected with enteropathogenic Escherichia coli (EPEC) had reduced MCP-1 levels (odds ratio = 0.38, 95% CI 0.18-0.80) compared with children in the placebo group. Among children not infected with Ascaris lumbricoides vitamin A supplemented children had reduced MCP-1 levels (OR = 0.62, 95% CI 0.41-0.94). These findings suggest that vitamin A has an anti-inflammatory effect in the gastrointestinal tract by reducing MCP-1 concentrations.

Multi-detector row CT urography of normal urinary collecting system: furosemide versus saline as adjunct to contrast medium.

PURPOSE: To retrospectively evaluate whether intravenous furosemide, either alone or in addition to intravenous saline, improved depiction of the normal urinary collecting system at multi-detector row computed tomographic (CT) urography. MATERIALS AND METHODS: Institutional review board approval for review of patient images and medical records was obtained; informed consent was not required for this HIPAA-compliant study. Excretory phase images from multi-detector row CT urography in 87 patients (44 women, 43 men; age range, 21-83 years; mean, 53 years) were reviewed. Examinations were performed with, in addition to intravenous contrast medium, 250 mL of intravenous normal saline alone (n = 35), both 250 mL of normal saline and 10 mg of intravenous furosemide (n = 26), or 10 mg of furosemide alone (n = 26). Three readers, blinded to the imaging technique used, individually assigned opacification scores to each of six urinary collecting system segments. Urinary distention was assessed by one reader by measuring transverse widths of the proximal, middle, and distal ureteral segments. Mean opacification scores for each segment and mean ureteral width measurements for each technique were compared by using the Student t test. RESULTS: Mean opacification scores achieved with furosemide were significantly higher than those achieved with saline for the middle (P

The effect of vitamin A supplementation on the intestinal immune response in Mexican children is modified by pathogen infections and diarrhea.

Vitamin A supplementation has consistently reduced infant mortality and the severity of pathogen-induced diarrhea. The mechanism by which vitamin A modulates the mucosal immune response to produce these effects remains poorly defined. To address this issue, stools collected during the summer months from 127 Mexican children 5-15 mo old enrolled in a larger, randomized, double-blind, placebo-controlled, vitamin A supplementation trial were screened for interleukin (IL)-4, IL-6, interferon-gamma (IFN-gamma), and gastrointestinal pathogens. Fecal cytokine values were categorized into 3 levels (undetectable, or =median). Multinomial regression models were used to determine the probability that vitamin A-supplemented children had higher categorical values of a cytokine than children in the placebo group. Differences in categorical values were also analyzed after stratification by gastrointestinal pathogen infections and diarrheal symptoms. Overall, fecal cytokine categorical levels did not differ between children randomized to the 2 arms. Vitamin A-supplemented children infected with enteropathogenic E. coli (EPEC) had reduced IL-4 and IFN-gamma levels [odds ratio (OR) = 0.3, 95% CI 0.13-0.67 and OR = 0.34, 95% CI 0.14-0.83, respectively] compared with children in the placebo group. Vitamin A-supplemented children had increased IL-4 levels when infected with A. lumbricoides (OR = 12.06, 95% CI 0.95-153.85). In contrast, IL-4 levels increased (OR = 2.14, 95% CI 0.94-4.87) and IFN-gamma levels decreased (OR = 0.51, 95% CI 0.26-0.99) among vitamin A-supplemented children with diarrhea compared with children in the placebo group. These findings suggest that the regulation of the mucosal immune response by vitamin A may depend on the type of enteric pathogen infecting the child and the presence of clinical symptoms.

Statistical combination schemes of repeated diagnostic test data.

RATIONALE AND OBJECTIVES: When diagnostic tests are repeated and combined, a number of schemes may be adopted. Guidelines for their interpretations are required. MATERIALS AND METHODS: Three combination schemes, "and" (A), "or" (O), and "majority" (M), are considered. To evaluate these schemes, dependency by specifying kappa values quantifying repeated test agreement was structured. In a pilot study, the combined accuracies of magnetic resonance imaging using six different pulse sequences of medial collateral ligaments of the elbows of 28 cadavers, with eight having lesions artificially created surgically, were examined. Images were evaluated simultaneously by using a five-point ordinal scale. For each pulse sequence, individuals for whom the diagnosis varied from once to three repetitions were considered. RESULTS: Scheme M improves diagnostic accuracy when sensitivity and specificity of a single test exceed 0.5, with maximal improvement at 0.79. Under scheme A, sensitivity decreases to 0.38-0.59. Under scheme O, sensitivity increases to 0.53-0.79. Scheme M yields a small improvement, reaching 0.50-0.71. Under scheme A, specificity increases to 0.95-0.98. Under scheme O, specificity decreases to 0.91-0.98. Scheme M also yields a small improvement, reaching 0.94-0.98. CONCLUSION: Scheme A is recommended for ruling in diagnoses, scheme O is recommended for ruling out diagnoses, and scheme M is neutral. Consequently, different schemes may be used to optimize the target diagnostic accuracy.

Supratentorial low-grade glioma resectability: statistical predictive analysis based on anatomic MR features and tumor characteristics.

PURPOSE: To retrospectively assess the main variables that affect the complete magnetic resonance (MR) imaging-guided resection of supratentorial low-grade gliomas. MATERIALS AND METHODS: Institutional review board approval was obtained for this retrospective HIPAA-compliant study, with the requirement for informed consent waived. Data from 101 patients (61 men, 40 women; mean age, 39 years; age range, 18-72 years) who had nonenhancing supratentorial mass lesions that were histopathologically diagnosed as low-grade (World Health Organization grade II) gliomas and consecutively underwent surgery with intraoperative MR imaging guidance were analyzed. There were 21 low-grade astrocytomas, 64 oligodendrogliomas, and 16 mixed oligoastrocytomas. Initial and residual tumor volumes were measured on intraoperative T2-weighted MR images and three-dimensional spoiled gradient-echo MR images. The anatomic relationships between the tumor and eloquent cortical and/or subcortical regions and the influence of these relationships on the extent of resection were analyzed on the basis of preoperative MR imaging findings. Summary measures, univariate Fisher exact test and t test, and multivariate logistic regression analyses were performed. RESULTS: Tumor volume ranged from 2.7-231.0 mL. Univariate analyses revealed the following tumor characteristics to be significant predictive variables of incomplete tumor resection: diffuse tumor margin on T2-weighted MR images, oligodendroglioma or oligoastrocytoma histopathologic type, and large tumor volume (P < .05 for all). Tumor involvement of the following structures was associated with incomplete resection: corpus callosum, corticospinal tract, insular lobe, middle cerebral artery, motor cortex, optic radiation, visual cortex, and basal ganglia (P < .05 for all). Multivariate analyses revealed that incomplete tumor resection was due to tumor involvement of the corticospinal tract (P < .01), large tumor volume (P < .01), and oligodendroglioma histopathologic type (P = .02). CONCLUSION: The main variables associated with incomplete tumor resection in 101 patients were identified by using statistical predictive analyses.

A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method.

OBJECTIVE: Medical classification accuracy studies often yield continuous data based on predictive models for treatment outcomes. A popular method for evaluating the performance of diagnostic tests is the receiver operating characteristic (ROC) curve analysis. The main objective was to develop a global statistical hypothesis test for assessing the goodness-of-fit (GOF) for parametric ROC curves via the bootstrap. DESIGN: A simple log (or logit) and a more flexible Box-Cox normality transformations were applied to untransformed or transformed data from two clinical studies to predict complications following percutaneous coronary interventions (PCIs) and for image-guided neurosurgical resection results predicted by tumor volume, respectively. We compared a non-parametric with a parametric binormal estimate of the underlying ROC curve. To construct such a GOF test, we used the non-parametric and parametric areas under the curve (AUCs) as the metrics, with a resulting p value reported. RESULTS: In the interventional cardiology example, logit and Box-Cox transformations of the predictive probabilities led to satisfactory AUCs (AUC=0.888; p=0.78, and AUC=0.888; p=0.73, respectively), while in the brain tumor resection example, log and Box-Cox transformations of the tumor size also led to satisfactory AUCs (AUC=0.898; p=0.61, and AUC=0.899; p=0.42, respectively). In contrast, significant departures from GOF were observed without applying any transformation prior to assuming a binormal model (AUC=0.766; p=0.004, and AUC=0.831; p=0.03), respectively. CONCLUSIONS: In both studies the p values suggested that transformations were important to consider before applying any binormal model to estimate the AUC. Our analyses also demonstrated and confirmed the predictive values of different classifiers for determining the interventional complications following PCIs and resection outcomes in image-guided neurosurgery.

The use of receiver operating characteristic curves in biomedical informatics.

Receiver operating characteristic (ROC) curves are frequently used in biomedical informatics research to evaluate classification and prediction models for decision support, diagnosis, and prognosis. ROC analysis investigates the accuracy of a model's ability to separate positive from negative cases (such as predicting the presence or absence of disease), and the results are independent of the prevalence of positive cases in the study population. It is especially useful in evaluating predictive models or other tests that produce output values over a continuous range, since it captures the trade-off between sensitivity and specificity over that range. There are many ways to conduct an ROC analysis. The best approach depends on the experiment; an inappropriate approach can easily lead to incorrect conclusions. In this article, we review the basic concepts of ROC analysis, illustrate their use with sample calculations, make recommendations drawn from the literature, and list readily available software.

Use of productivity and financial indicators for monitoring performance in academic radiology departments: U.S. nationwide survey.

PURPOSE: To determine how productivity- and finance-related indicators are used by radiology departments to evaluate departmental performance. MATERIALS AND METHODS: The study met the criteria to be exempt from institutional review board approval. All subjects were informed of the purpose of the study and that their questionnaire responses would be kept confidential. For the study, a survey was sent to 132 members of the Society of Chairmen of Academic Radiology Departments (SCARD) nationwide. The survey was designed to (a) assess organizational information about hospital and radiology departments, (b) determine the types and mean numbers of productivity and financial indicators used by radiology departments, (c) determine how these indicators are used to influence departmental productivity, and (d) assess the reference-standard goals with which each indicator value was compared. A total of 77 variables were studied. Summary statistics, Spearman rank correlation coefficient, and chi2 analyses were performed. RESULTS: The response rate was 42% (55 of 132 surveyed SCARD members). The mean number of productivity indicators used by radiology departments was 4.55 +/- 2.56 (standard deviation), while the mean number of financial indicators used was 2.89 +/- 1.99. Twenty-two (40%) of the 55 responding departments used productivity indicators to monitor and provide feedback to radiologists, hospital leaders, and technical staff members for improved productivity, but only 11 (20%) departments used these indicators to compare personnel performances against specific productivity standards. The most frequent goal (of seven [13%] responding departments) of using the indicators was to increase the examination volume from the previous year by 5%-10%. CONCLUSION: Academic radiology departments across the United States do not use a standardized set of productivity and financial indicators to measure departmental performance. Examination volume is the most frequently used productivity indicator, whereas general expenses are commonly used as indicators of financial status.

Statistical validation of brain tumor shape approximation via spherical harmonics for image-guided neurosurgery.

RATIONALE AND OBJECTIVES: Surgical planning now routinely uses both two-dimensional (2D) and three-dimensional (3D) models that integrate data from multiple imaging modalities, each highlighting one or more aspects of morphology or function. We performed a preliminary evaluation of the use of spherical harmonics (SH) in approximating the 3D shape and estimating the volume of brain tumors of varying characteristics. MATERIALS AND METHODS: Magnetic resonance (MR) images from five patients with brain tumors were selected randomly from our MR-guided neurosurgical practice. Standardized mean square reconstruction errors (SMSRE) by tumor volume were measured. Validation metrics for comparing performances of the SH method against segmented contours (SC) were the dice similarity coefficient (DSC) and standardized Euclidean distance (SED) measure. RESULTS: Tumor volume range was 22,413-85,189 mm3, and range of number of vertices in triangulated models was 3674-6544. At SH approximations with degree of at least 30, SMSRE were within 1.66 x 10(-5) mm(-1). Summary measures yielded a DSC range of 0.89-0.99 (pooled median, 0.97 and significantly >0.7; P < .001) and an SED range of 0.0002-0.0028 (pooled median, 0.0005). CONCLUSION: 3D shapes of tumors may be approximated by using SH for neurosurgical applications.

Combining classifiers using their receiver operating characteristics and maximum likelihood estimation.

In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging.

Practice management performance indicators in academic radiology departments.

PURPOSE: To determine the management performance indicators most frequently utilized in academic radiology departments in the United States. MATERIALS AND METHODS: This investigation met the criteria for an exemption from institutional review board approval. A cross-sectional study in which a validated national survey was sent to members of the Society of Chairmen of Academic Radiology Departments (SCARD) was conducted. The survey was designed to examine the following six categories of 28 performance indicators: (a) general organization, (b) volume and productivity, (c) radiology reporting, (d) access to examinations, (e) customer satisfaction, and (f) finance. A total of 158 variables were included in the analysis. Summary statistics, the chi(2) test, rank correlation, multiple regression analysis, and analysis of variance were used. RESULTS: A response rate of 42% (55 of 132 SCARD members) was achieved. The mean number of performance indicators used by radiology departments was 16 +/- 6.35 (standard deviation). The most frequently utilized performance indicators were as follows: (a) productivity, in terms of examination volume (78% [43 departments]) and examination volume per modality (78% [43 departments]); (b) reporting, in terms of report turnaround (82% [45 departments]) and transcription time (71% [39 departments]); (c) access, in terms of appointment access to magnetic resonance imaging (80% [44 departments]); (d) satisfaction, in terms of number of patient complaints (84% [46 departments]); and (e) finance, in terms of expenses (67% [37 departments]). Regression analysis revealed that the numbers of performance indicators in each category were statistically significant in predicting the total number of performance indicators used (P < .001 for all). Numbers of productivity and financial indicators were moderately correlated (r = 0.51). However, there were no statistically significant correlations between the numbers of performance indicators used and hospital location, hospital size, or department size (P > .4 for all). CONCLUSION: Assessing departmental performance with a wide range of management indicators is not yet an established and standardized practice in academic radiology departments in the United States. Among all indicators, productivity indicators are the most frequently used.

Motivation and compensation in academic radiology.

As radiologists are increasingly faced with the challenges of rising demand for imaging services and staff shortages, the implementation of incentive plans in radiology is gaining importance. A key factor to be considered while developing an incentive plan is the strategic goal of the department. In academic radiology, management should decide whether it will reward research and teaching productivity in addition to clinical productivity. Various models have been suggested for incentive plans based on (1) clinical productivity, (2) multifactor productivity, (3) individual productivity, (4) section productivity, and (5) chair's discretion. Although fiscal rewards are most common, managers should consider other incentives, such as research time, resources for research, vacation time, and recognition awards, because academic radiologists may be motivated by factors other than financial gains.

Essential practice performance measurement.

The objective of this paper is to provide an overview of how to develop and implement a performance measurement system in radiology departments. Although an extensive literature review (PubMed, MEDLINE, etc) was carried out to search for relevant published scientific papers, the number of publications regarding performance indicators in radiology departments was very limited. The present paper reflects the current approach to performance measurement in health care services based on the available literature, which may be applied to the field of radiology. Performance indicators are tools that evaluate an organizations progress toward its goals . In radiology, in addition to finance, other aspects that affect the functioning of the organization, such as clinical productivity and patient satisfaction, also need to be assessed. The main categories of indicators adopted in radiology departments include: (1) productivity, (2) finance, (3) patient safety, (4) access, and (5) customer satisfaction. Once specific indicators in each of these categories are selected, the data collection methods should be incorporated into the routine departmental processes. Information obtained should be made available to all stakeholders via various media. In conclusion, performance indicators establish a common denominator in order to make comaparisons of the organization's performance over time. To improve the quality of services, these indicators should be benchmarked, i.e., the processes should be compared to the best in the field.

Clinical operations management in radiology.

Providing radiology services is a complex and technically demanding enterprise in which the application of operations management (OM) tools can play a substantial role in process management and improvement. This paper considers the benefits of an OM process in a radiology setting. Available techniques and concepts of OM are addressed, along with gains and benefits that can be derived from these processes. A reference framework for the radiology processes is described, distinguishing two phases in the initial assessment of a unit: the diagnostic phase and the redesign phase.