Examining the Agreement of Structural Loss Measured With Heidelberg Retinal Tomograhy and Functional Loss With Standard Automated Perimetry (SAP; Octopus), Pulsar Perimetry (PP), and Moorfields Motion Displacement Test (MDT) Perimetry

Purpose: To examine the relationship of functional measurements with structural measures. Methods: 146 eyes of 83 test subjects underwent Heidelberg Retinal Tomography (HRTIII) (disc area<2.43, mphsd<40), and perimetry testing with Octopus (SAP; Dynamic), Pulsar (PP; TOP) and Moorfields MDT (ESTA). Glaucoma was defined as progressive structural or functional loss (20 eyes). Perimetry test points were grouped into 6 sectors based on the estimated optic nerve head angle into which the associated nerve fiber bundle enters (Garway-Heath map). Perimetry summary measures (PSM) (MD SAP/ MD PP/ PTD MDT) were calculated from the average total deviation of each measured threshold from the normal for each sector. We calculated the 95% significance level of the sectorial PSM from the respective normative data. We calculated the percentage agreement with group1 (G1), healthy on HRT and within normal perimetric limits, and group 2 (G2), abnormal on HRT and outside normal perimetric limits. We also examined the relationship of PSM and rim area (RA) in those sectors classified as abnormal by MRA (Moorfields Regression Analysis) of HRT. Results: The mean age was 65 (range= [37, 89]). The global sensitivity versus specificity of each instrument in detecting glaucomatous eyes was: MDT 80% vs. 88%, SAP 80% vs. 80%, PP 70% vs. 89% and HRT 80% vs. 79%. Highest percentage agreement of HRT (respectively G1, G2, sector) with PSM were MDT (89%, 57%, nasal superior), SAP (83%, 74%, temporal superior), PP (74%, 63%, nasal superior). Globally percentage agreement (respectively G1, G2) was MDT (92%, 28%), SAP (87%, 40%) and PP (77%, 49%). Linear regression showed there was no significant trend globally associating RA and PSM. However, sectorally the supero-nasal sector had a statistically significant (p<0.001) trend with each instrument, the associated r2 coefficients are (MDT 0.38 SAP 0.56 and PP 0.39). Conclusions: There were no significant differences in global sensitivity or specificity between instruments. Structure-function relationships varied significantly between instruments and were consistently strongest supero-nasally. Further studies are required to investigate these relationships in detail.

L'erreur en médecine ambulatoire: comment l'aborder. [Error in ambulatory medicine: how to deal with it?]

Medical errors compromise patient safety in ambulatory practice. These errors must be faced in a framework that reduces to a minimum their consequences for the patients. This approach relies on the implementation of a new culture without stigmatization and where errors are disclosed to the patients; this culture implies the build up of a system for reporting errors associated to an in-depth analysis of the system, looking for root causes and insufficient barriers with the aim to fix them. A useful education tool is the "critical situations" meeting during which physicians are encouraged to openly present adverse events and "near misses". Their analysis, with supportive attitude towards involved staff members, allows to reveal systems failures within the institution or the private practice.

SPOC Collective Bargaining Agreement 2007-2009, July 2007

This Agreement made and entered into this 1st day of July 2007 at Des Moines, Iowa, pursuant to the provisions of Chapter 20 of the Iowa Code, by and between the State of Iowa (hereinafter referred to as the Employer) and the State Police Officers Council, and its appropriate affiliated sub-organizations (hereinafter referred to as the Council), as representatives of employees employed by the State of Iowa, as set forth specifically in the Recognition Clause.

AFSCME Collective Bargaining Agreement 2007-2009, July 2007

This Agreement made and entered into this 1st day of July 2007, at Des Moines, Iowa, pursuant to the provisions of Chapter 20 of the Code of Iowa, by and between the State of Iowa (hereinafter referred to as the Employer) and the American Federation of State, County, and Municipal Employees, Iowa Public Employees Council 61, AFL-CIO (hereinafter referred to as the Union), and its appropriate affiliated locals, as representatives of employees employed by the State of Iowa, as set forth specifically in Appendix A.

IUP Science Unit collective Bargainging Agreement, July 2007

This Agreement made and entered i into this 1 1st day of July 2007, at Des Moines, Iowa, pursuant to the provisions of Chapter 20 of the Iowa Code, by and between the State of Iowa (hereinafter referred to as the Employer) and UE Local 893/Iowa United Professionals, and its appropriate affiliated locals, as representatives of employees employed by the State of Iowa, as set forth specifically in Appendix A (hereinafter referred to as the Union).

IUP Social Services Collective Bargaining Agreement, July 2007

This agreement made and entered in to this 1st day of July 2007, at Des Moines, Iowa pursuant to the provisions of Chapter 20 of the Iowa code, by and between the State of Iowa (hereinafter referred to as the Employer) and UE Local 893/Iowa United Professionals, and it's appropriate affiliated locals, as representatives of employees employed by the State of Iowa, as set forth specifically in appendix A (hereinafter referred to as the Union).

Model selection and error estimation

We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of the error estimate. We consider several penalty functions, involving error estimates on independent test data, empirical {\sc vc} dimension, empirical {\sc vc} entropy, andmargin-based quantities. We also consider the maximal difference between the error on the first half of the training data and the second half, and the expected maximal discrepancy, a closely related capacity estimate that can be calculated by Monte Carlo integration. Maximal discrepancy penalty functions are appealing for pattern classification problems, since their computation is equivalent to empirical risk minimization over the training data with some labels flipped.

Random measurement error and regression dilution bias.

Summary points: - The bias introduced by random measurement error will be different depending on whether the error is in an exposure variable (risk factor) or outcome variable (disease) - Random measurement error in an exposure variable will bias the estimates of regression slope coefficients towards the null - Random measurement error in an outcome variable will instead increase the standard error of the estimates and widen the corresponding confidence intervals, making results less likely to be statistically significant - Increasing sample size will help minimise the impact of measurement error in an outcome variable but will only make estimates more precisely wrong when the error is in an exposure variable

Reliable tidal volume estimates at the airway opening with an infant monitor during high-frequency oscillatory ventilation.

OBJECTIVE: To assess the suitability of a hot-wire anemometer infant monitoring system (Florian, Acutronic Medical Systems AG, Hirzel, Switzerland) for measuring flow and tidal volume (Vt) proximal to the endotracheal tube during high-frequency oscillatory ventilation. DESIGN: In vitro model study. SETTING: Respiratory research laboratory. SUBJECT: In vitro lung model simulating moderate to severe respiratory distress. INTERVENTION: The lung model was ventilated with a SensorMedics 3100A ventilator. Vt was recorded from the monitor display (Vt-disp) and compared with the gold standard (Vt-adiab), which was calculated using the adiabatic gas equation from pressure changes inside the model. MEASUREMENTS AND MAIN RESULTS: A range of Vt (1-10 mL), frequencies (5-15 Hz), pressure amplitudes (10-90 cm H2O), inspiratory times (30% to 50%), and Fio2 (0.21-1.0) was used. Accuracy was determined by using modified Bland-Altman plots (95% limits of agreement). An exponential decrease in Vt was observed with increasing oscillatory frequency. Mean DeltaVt-disp was 0.6 mL (limits of agreement, -1.0 to 2.1) with a linear frequency dependence. Mean DeltaVt-disp was -0.2 mL (limits of agreement, -0.5 to 0.1) with increasing pressure amplitude and -0.2 mL (limits of agreement, -0.3 to -0.1) with increasing inspiratory time. Humidity and heating did not affect error, whereas increasing Fio2 from 0.21 to 1.0 increased mean error by 6.3% (+/-2.5%). CONCLUSIONS: The Florian infant hot-wire flowmeter and monitoring system provides reliable measurements of Vt at the airway opening during high-frequency oscillatory ventilation when employed at frequencies of 8-13 Hz. The bedside application could improve monitoring of patients receiving high-frequency oscillatory ventilation, favor a better understanding of the physiologic consequences of different high-frequency oscillatory ventilation strategies, and therefore optimize treatment.

2009-2011 Collective Bargaining Agreement Between State of Iowa and Local 893; Science Unit, July 1, 2009

This Agreement made and entered i into this 1 1st day of July 2009, at Des Moines, Iowa, pursuant to the provisions of Chapter 20 of the Iowa Code, by and between the State of Iowa (hereinafter referred to as the Employer) and UE Local 893/Iowa United Professionals, and its appropriate affiliated locals, as representatives of employees employed by the State of Iowa, as set forth specifically in Appendix A (hereinafter referred to as the Union).

2009-2011 Collective Bargaining Agreement Between State of Iowa and Local 893; Social Services, July 1, 2009

This Agreement made and entered i into this 1 1st day of July 2009, at Des Moines, Iowa, pursuant to the provisions of Chapter 20 of the Iowa Code, by and between the State of Iowa (hereinafter referred to as the Employer) and UE Local 893/Iowa United Professionals, and its appropriate affiliated locals, as representatives of employees employed by the State of Iowa.