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Training Time (training + time)
Selected AbstractsThe Status of Bedside Ultrasonography Training in Emergency Medicine Residency ProgramsACADEMIC EMERGENCY MEDICINE, Issue 1 2003Francis L. Counselman MD Abstract Bedside ultrasonography (BU) is rapidly being incorporated into emergency medicine (EM) training programs and clinical practice. In the past decade, several organizations in EM have issued position statements on the use of this technology. Program training content is currently driven by the recently published "Model of the Clinical Practice of Emergency Medicine," which includes BU as a necessary skill. Objective: The authors sought to determine the current status of BU training in EM residency programs. Methods: A survey was mailed in early 2001 to all 122 Accreditation Council for Graduate Medical Education (ACGME)-accredited EM residency programs. The survey instrument asked whether BU was currently being taught, how much didactic and hands-on training time was incorporated into the curriculum, and what specialty representation was present in the faculty instructors. In addition, questions concerning the type of tests performed, the number considered necessary for competency, the role of BU in clinical decision making, and the type of quality assurance program were included in the survey. Results: A total of 96 out of 122 surveys were completed (response rate of 79%). Ninety-one EM programs (95% of respondents) reported they teach BU, either clinically and/or didactically, as part of their formal residency curriculum. Eighty-one (89%) respondents reported their residency program or primary hospital emergency department (ED) had a dedicated ultrasound machine. BU was performed most commonly for the following: the FAST scan (focused abdominal sonography for trauma, 79/87%); cardiac examination (for tamponade, pulseless electrical activity, etc., 65/71%); transabdominal (for intrauterine pregnancy, ectopic pregnancy, etc., 58/64%); and transvaginal (for intrauterine pregnancy, ectopic pregnancy, etc., 45/49%). One to ten hours of lecture on BU was provided in 43%, and one to ten hours of hands-on clinical instruction was provided in 48% of the EM programs. Emergency physicians were identified as the faculty most commonly involved in teaching BU to EM residents (86/95%). Sixty-one (69%) programs reported that EM faculty and/or residents made clinical decisions and patient dispositions based on the ED BU interpretation alone. Fourteen (19%) programs reported that no formal quality assurance program was in place. Conclusions: The majority of ACGME-accredited EM residency programs currently incorporate BU training as part of their curriculum. The majority of BU instruction is done by EM faculty. The most commonly performed BU study is the FAST scan. The didactic component and clinical time devoted to BU instruction are variable between programs. Further standardization of training requirements between programs may promote increasing standardization of BU in future EM practice. [source] Brain natriuretic peptide and the athlete's heart: a pilot studyINTERNATIONAL JOURNAL OF CLINICAL PRACTICE, Issue 4 2010E. D. Pagourelias Background:, The role of brain natriuretic peptide (BNP) in differentiating the athlete's heart from maladaptive cardiac hypertrophy is unclear. Methods:, To address this issue, an integrated M mode, two-dimensional B mode and Doppler echocardiographical study were performed and plasma BNP levels were measured in 25 strength athletes, 25 patients with established hypertrophic cardiomyopathy (HCM) and 25 healthy volunteers. Results:, Among athletes, BNP levels correlated negatively with the total training time (r = ,0.79, p = 0.002) and positively with ejection fraction (r = 0.58, p = 0.049) and fractional shortening (r = 0.57, p = 0.049). A BNP cut-off value of 11.8 pg/ml had 88% specificity and 74% negative predictive value for the exclusion of HCM. Conclusions:, Brain natriuretic peptide might be useful as a preparticipation screening test in athletes. [source] Application of a genetic algorithm in an artificial neural network to calculate the resonant frequency of a tunable single-shorting-post rectangular-patch antennaINTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING, Issue 1 2005Shyam S. Pattnaik Abstract In this article, an efficient application of a genetic algorithm (GA) in an artificial neural network (ANN) to calculate the resonant frequency of a coaxially-fed tunable rectangular microstrip-patch antenna is presented. For a normal feed-forward back-propagation algorithm, with a compromise between time and accuracy, it is difficult to train the network to achieve an acceptable error tolerance. The selection of suitable parameters of ANNs in a feed-forward network leads to a high number of man-hours necessary to train a network efficiently. However, in the present method, the GA is used to reduce the man-hours while training a neural network using the feed forward-back-propagation algorithm. It is seen that the training time has also been reduced to a great extent while giving high accuracy. The results are in very good agreement with the experimental results. © 2004 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2005. [source] TRAINING EFFECTS ON PERFORMANCE OF DESCRIPTIVE PANELISTSJOURNAL OF SENSORY STUDIES, Issue 6 2004DELORES H. CHAMBERS ABSTRACT The amount of training necessary to adequately "train" a descriptive panel is a matter of contention. The objectives of this research were to compare the performance of descriptive panelists after short-term (4 h), moderate (60 h) and extensive training (120 h). Seven screened panelists were chosen to evaluate three commercial tomato pasta sauces after each training period. Panelist performance improved with increased training. Sample differences were observed in all texture attributes and some flavor attributes, even after the shortest training time (4 h). However, more differences were found with at least 60 h of product training. After 120 h of training, product differences for all texture attributes and most of the flavor attributes studied could be ascertained by the trained panelists. These results suggested that only limited training may be necessary to find differences among products for many texture attributes and some flavor attributes. However, extensive training may be required to reduce variation among panelists and increase the discriminant abilities of panelists. [source] Field-Scale Application of Three Types of Neural Networks to Predict Ground-Water Levels,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 5 2007Tirusew Asefa Abstract:, In this paper, a field-scale applicability of three forms of artificial neural network algorithms in forecasting short-term ground-water levels at specific control points is presented. These algorithms are the feed-forward back propagation (FFBP), radial basis networks (RBN), and generalized regression networks (GRN). Ground-water level predictions from these algorithms are in turn to be used in an Optimized Regional Operations Plan that prescribes scheduled wellfield production for the coming four weeks. These models are up against each other for their accuracy of ground-water level predictions on lead times ranging from a week to four weeks, ease of implementation, and execution times (mainly training time). In total, 208 networks of each of the three algorithms were developed for the study. It is shown that although learning algorithms have emerged as a viable solution at field scale much larger than previously studied, no single algorithm performs consistently better than others on all the criteria. On average, FFBP networks are 20 and 26%, respectively, more accurate than RBN and GRN in forecasting one week ahead water levels and this advantage drops to 5 and 9% accuracy in forecasting four weeks ahead water levels, whereas GRN posted a training time that is only 5% of the training time taken by that of FFBP networks. This may suggest that in field-scale applications one may have to trade between the type of algorithm to be used and the degree to which a given objective is honored. [source] Assessing suturing techniques using a virtual reality surgical simulatorMICROSURGERY, Issue 6 2010Hamed Kazemi M.Eng. Advantages of virtual-reality simulators surgical skill assessment and training include more training time, no risk to patient, repeatable difficulty level, reliable feedback, without the resource demands, and ethical issues of animal-based training. We tested this for a key subtask and showed a strong link between skill in the simulator and in reality. Suturing performance was assessed for four groups of participants, including experienced surgeons and naive subjects, on a custom-made virtual-reality simulator. Each subject tried the experiment 30 times using five different types of needles to perform a standardized suture placement task. Traditional metrics of performance as well as new metrics enabled by our system were proposed, and the data indicate difference between trained and untrained performance. In all traditional parameters such as time, number of attempts, and motion quantity, the medical surgeons outperformed the other three groups, though differences were not significant. However, motion smoothness, penetration and exit angles, tear size areas, and orientation change were statistically significant in the trained group when compared with untrained group. This suggests that these parameters can be used in virtual microsurgery training. © 2010 Wiley-Liss, Inc. Microsurgery 30:479,486, 2010. [source] Gaps in Procedural Experience and Competency in Medical School GraduatesACADEMIC EMERGENCY MEDICINE, Issue 2009Susan B. Promes MD Abstract Objectives:, The goal of undergraduate medical education is to prepare medical students for residency training. Active learning approaches remain important elements of the curriculum. Active learning of technical procedures in medical schools is particularly important, because residency training time is increasingly at a premium because of changes in the Accreditation Council for Graduate Medical Education duty hour rules. Better preparation in medical school could result in higher levels of confidence in conducting procedures earlier in graduate medical education training. The hypothesis of this study was that more procedural training opportunities in medical school are associated with higher first-year resident self-reported competency with common medical procedures at the beginning of residency training. Methods:, A survey was developed to assess self-reported experience and competency with common medical procedures. The survey was administered to incoming first-year residents at three U.S. training sites. Data regarding experience, competency, and methods of medical school procedure training were collected. Overall satisfaction and confidence with procedural education were also assessed. Results:, There were 256 respondents to the procedures survey. Forty-four percent self-reported that they were marginally or not adequately prepared to perform common procedures. Incoming first-year residents reported the most procedural experience with suturing, Foley catheter placement, venipuncture, and vaginal delivery. The least experience was reported with thoracentesis, central venous access, and splinting. Most first-year residents had not provided basic life support, and more than one-third had not performed cardiopulmonary resuscitation (CPR). Participation in a targeted procedures course during medical school and increasing the number of procedures performed as a medical student were significantly associated with self-assessed competency at the beginning of residency training. Conclusions:, Recent medical school graduates report lack of self-confidence in their ability to perform common procedures upon entering residency training. Implementation of a medical school procedure course to increase exposure to procedures may address this challenge. [source] Forecasting Models of Emergency Department CrowdingACADEMIC EMERGENCY MEDICINE, Issue 4 2009Lisa M. Schweigler MD Abstract Objectives:, The authors investigated whether models using time series methods can generate accurate short-term forecasts of emergency department (ED) bed occupancy, using traditional historical averages models as comparison. Methods:, From July 2005 through June 2006, retrospective hourly ED bed occupancy values were collected from three tertiary care hospitals. Three models of ED bed occupancy were developed for each site: 1) hourly historical average, 2) seasonal autoregressive integrated moving average (ARIMA), and 3) sinusoidal with an autoregression (AR)-structured error term. Goodness of fits were compared using log likelihood and Akaike's Information Criterion (AIC). The accuracies of 4- and 12-hour forecasts were evaluated by comparing model forecasts to actual observed bed occupancy with root mean square (RMS) error. Sensitivity of prediction errors to model training time was evaluated, as well. Results:, The seasonal ARIMA outperformed the historical average in complexity adjusted goodness of fit (AIC). Both AR-based models had significantly better forecast accuracy for the 4- and the 12-hour forecasts of ED bed occupancy (analysis of variance [ANOVA] p < 0.01), compared to the historical average. The AR-based models did not differ significantly from each other in their performance. Model prediction errors did not show appreciable sensitivity to model training times greater than 7 days. Conclusions:, Both a sinusoidal model with AR-structured error term and a seasonal ARIMA model were found to robustly forecast ED bed occupancy 4 and 12 hours in advance at three different EDs, without needing data input beyond bed occupancy in the preceding hours. [source] | |||||||||||||