What orthopaedic surgery residents need to know about the hand and wrist?

<p>Abstract</p> <p>Background</p> <p>To develop a Core Curriculum for Orthopaedic Surgery; and to conduct a national survey to assess the importance of curriculum items as judged by orthopaedic surgeons with primary affiliation non-academic. Attention for this manuscript was focused on determining the importance of topics pertaining to adult hand and wrist reconstruction.</p> <p>Methods</p> <p>A 281-item questionnaire was developed and consisted of three sections: 1) Validated Musculoskeletal Core Curriculum; 2) Royal College of Physician and Surgeons of Canada (RCPSC) Specialty Objectives and; 3) A procedure list. A random group of 131 [out of 156] orthopaedic surgeons completed the questionnaire. Data were analyzed descriptively and quantitatively using histograms, a Modified Hotel ling's T²-statistic <abbrgrp><abbr bid="B1">1</abbr></abbrgrp> with p-value determined by a permutation test, and the Benjamini-Hochberg/Yekutieli procedure</p> <p>Results</p> <p>131/156 (84%) orthopaedic surgeons participated in this study. 27/32 items received an average mean score of at least 3.0/4.0 by all respondents thus suggesting that 84% of the items are either "probably important" or "important" to know by the end of residency (SD range 0.007–0.228). The Benjamini-Hochberg procedure demonstrated that for 80% of the 32 × 31/2 = 496 possible pairs of hand and wrist questions did not appear to demonstrate the same distribution of ratings given that one question was different from that of another question.</p> <p>Conclusion</p> <p>This study demonstrates with reliable statistical evidence, agreement on the importance of 27/32 items pertaining to hand and wrist reconstruction is included in a Core Curriculum for Orthopaedic Surgery. Residency training programs need ensure that educational opportunities focusing on the ability to perform with proficiency procedures pertaining to the hand and wrist is taught and evaluated in their respective programs.</p

Statistical modeling of valley fever data in Kern County, California.

Coccidioidomycosis (valley fever) is a fungal infection found in the southwestern US, northern Mexico, and some places in Central and South America. The fungus that causes it (Coccidioides immitis) is normally soil-dwelling but, if disturbed, becomes air-borne and infects the host when its spores are inhaled. It is thus natural to surmise that weather conditions that foster the growth and dispersal of the fungus must have an effect on the number of cases in the endemic areas. We present here an attempt at the modeling of valley fever incidence in Kern County, California, by the implementation of a generalized auto regressive moving average (GARMA) model. We show that the number of valley fever cases can be predicted mainly by considering only the previous history of incidence rates in the county. The inclusion of weather-related time sequences improves the model only to a relatively minor extent. This suggests that fluctuations of incidence rates (about a seasonally varying background value) are related to biological and/or anthropogenic reasons, and not so much to weather anomalies

ORIGINAL ARTICLE Statistical modeling of valley fever data in Kern County,

Abstract Coccidioidomycosis (valley fever) is a fungal infection found in the southwestern US, northern Mexico, and some places in Central and South America. The fungus that causes it (Coccidioides immitis) is normally soildwelling but, if disturbed, becomes air-borne and infects the host when its spores are inhaled. It is thus natural to surmise that weather conditions that foster the growth and dispersal of the fungus must have an effect on the number of cases in the endemic areas. We present here an attempt at the modeling of valley fever incidence in Kern County, California, by the implementation of a generalized auto regressive moving average (GARMA) model. We show that the number of valley fever cases can be predicted mainly by considering only the previous history of incidence rates in the county. The inclusion of weather-related time sequences improves the model only to a relatively minor extent. This suggests that fluctuations of incidence rates (about a seasonally varying background value) are related to biological and/or anthropogenic reasons, and not so much to weather anomalies

Fluctuations in Climate and Incidence of Coccidioidomycosis in Kern County, California: A Review

Coccidioidomycosis (Valley Fever) is a fungal infection found in the southwestern United States, northern Mexico, and some places in Central and South America. The fungi that cause it (Coccidioides immitis and Coccidioides posadasii) are normally soil dwelling, but, if disturbed, become airborne and infect the host when their spores are inhaled. It is thus natural to surmise that weather conditions, which foster the growth and dispersal of Coccidioides, must have an effect on the number of cases in the endemic areas. This article reviews our attempts to date at quantifying this relationship in Kern County, California (where C. immitisis endemic). We have examined the effect on incidence resulting from precipitation, surface temperature, and wind speed. We have performed our studies by means of a simple linear correlation analysis, and by a generalized autoregressive moving average model. Our first analysis suggests that linear correlations between climatic parameters and incidence are weak; our second analysis indicates that incidence can be predicted largely by considering only the previous history of incidence in the county—the inclusion of climate- or weather-related time sequences improves the model only to a relatively minor extent. Our work therefore suggests that incidence fluctuations (about a seasonally varying background value) are related to biological and/or anthropogenic reasons, and not so much to weather or climate anomalies

Statistical Modeling of Valley Fever Data in Kern County, California ∗

Abstract. Coccidioidomycosis (valley fever) is a fungal infection found in the southwestern US, northern Mexico, and some places in central and South America. The fungus which causes it (Coccidioides immitis) is normally soil-dwelling but, if disturbed, becomes air-borne and infects the host when its spores are inhaled. It is thus natural to surmise that weather conditions which foster the growth and dispersal of the fungus must have an effect on the number of cases in the endemic areas. We present here an attempt at the modeling of valley fever incidence in Kern County, California by the implementation of a Generalized Auto Regressive Moving Average (GARMA) model. We show that the number of valley fever cases can be predicted mainly by considering only the previous history of incidence rates in the county. The inclusion of weather-related time sequences improves the model only to a relatively minor extent. This suggests that fluctuations of incidence rates (about a seasonally-varying background value) are related to biological and/or anthropogenic reasons, and not so much to weather anomalies

2.2 DOES CLIMATE CONTROL VALLEY FEVER INCIDENCE IN CALIFORNIA?

Coccidioidomycosis is a systemic infection caused by inhalation of airborne spores of Coccidioides immitis, a soildwellin

Distance-based differential analysis of gene curves

Motivation: Time course gene expression experiments are performed to study time-varying changes in mRNA levels of thousands of genes. Statistical methods from functional data analysis (FDA) have recently gained popularity for modelling and exploring such time courses. Each temporal profile is treated as the realization of a smooth function of time, or curve, and the inferred curve becomes the basic unit of statistical analysis. The task of identifying genes with differential temporal profiles then consists of detecting statistically significant differences between curves, where such differences are commonly quantified by computing the area between the curves or the l2 distance. Results: We propose a general test statistic for detecting differences between gene curves, which only depends on a suitably chosen distance measure between them. The test makes use of a distance-based variance decomposition and generalizes traditional MANOVA tests commonly used for vectorial observations. We also introduce the visual l2 distance, which is shown to capture shape-related differences in gene curves and is robust against time shifts, which would otherwise inflate the traditional l2 distance. Other shape-related distances, such as the curvature, may carry biological significance. We have assessed the comparative performance of the test on realistically simulated datasets and applied it to human immune cell responses to bacterial infection over time

Toward Exploiting EEG Input in a Reading Tutor

Abstract. A new type of sensor for students ’ mental states is a single-channel EEG headset simple enough to use in schools. Using its signal from adults and children reading text and isolated words, both aloud and silently, we train and test classifiers to tell easy from hard sentences, and to distinguish among easy words, hard words, pseudo-words, and unpronounceable strings. We also identify which EEG components appear sensitive to which lexical features. Better-than-chance performance shows promise for tutors to use EEG at school