Key performance indicators for successful simulation projects

There are many factors that may contribute to the successful delivery of a simulation project. To provide a structured approach to assessing the impact various factors have on project success, we propose a top-down framework whereby 15 Key Performance Indicators (KPI) are developed that represent the level of successfulness of simulation projects from various perspectives. They are linked to a set of Critical Success Factors (CSF) as reported in the simulation literature. A single measure called Project’s Success Measure (PSM), which represents the project’s total success level, is proposed. The framework is tested against 9 simulation exemplar cases in healthcare and this provides support for its reliability. The results suggest that responsiveness to the customer’s needs and expectations, when compared with other factors, holds the strongest association with the overall success of simulation projects. The findings highlight some patterns about the significance of individual CSFs, and how the KPIs are used to identify problem areas in simulation projects

The Association between Sleep Duration and Serum Leptin and Ghrelin Levels

<div><p>(A) Mean leptin levels and standard errors for half-hour increments of average nightly sleep after adjustment for age, sex, BMI, and time of storage (see <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0010062#pmed-0010062-t002" target="_blank">Table 2</a>). Average nightly sleep values outside the lowest and highest intervals are included in those categories. Sample sizes are given below the standard error bars. The y-axis uses a square-root scale. Data derived from 718 diaries because three participants had missing leptin data.</p> <p>(B) Mean ghrelin levels and standard errors for half-hour increments of total sleep time after adjustment for age, sex, BMI, and time of storage (see <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0010062#pmed-0010062-t002" target="_blank">Table 2</a>). Total sleep time values outside the lowest and highest intervals are included in those categories. The y-axis uses a square-root scale. Note that ranges for total sleep time amounts are typically shorter than those for average nightly sleep amounts (A; see <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0010062#pmed-0010062-g001" target="_blank">Figure 1</a>), and do not correlate strongly (see text).</p></div

The Relationship between BMI and Average Nightly Sleep

<p>Mean BMI and standard errors for 45-min intervals of average nightly sleep after adjustment for age and sex. Average nightly sleep values predicting lowest mean BMI are represented by the central group. Average nightly sleep values outside the lowest and highest intervals are included in those categories. Number of visits is indicated below the standard error bars. Standard errors are adjusted for within-subject correlation.</p

Stability of anti-streptococcal antibodies status across time.

<p>1118 individuals had 2 subsequent visits in a 4-year interval. Results in the second visit are stratified by the first visit results.</p><p>*Negative for both human PDI and Bovine PDI;</p>†<p>Positive for bovine PDI and/or human PDI.</p

Design and Validation of a Periodic Leg Movement Detector

<div><p>Periodic Limb Movements (PLMs) are episodic, involuntary movements caused by fairly specific muscle contractions that occur during sleep and can be scored during nocturnal polysomnography (NPSG). Because leg movements (LM) may be accompanied by an arousal or sleep fragmentation, a high PLM index (i.e. average number of PLMs per hour) may have an effect on an individual’s overall health and wellbeing. This study presents the design and validation of the Stanford PLM automatic detector (S-PLMAD), a robust, automated leg movement detector to score PLM. NPSG studies from adult participants of the Wisconsin Sleep Cohort (WSC, n = 1,073, 2000–2004) and successive Stanford Sleep Cohort (SSC) patients (n = 760, 1999–2007) undergoing baseline NPSG were used in the design and validation of this study. The scoring algorithm of the S-PLMAD was initially based on the 2007 American Association of Sleep Medicine clinical scoring rules. It was first tested against other published algorithms using manually scored LM in the WSC. Rules were then modified to accommodate baseline noise and electrocardiography interference and to better exclude LM adjacent to respiratory events. The S-PLMAD incorporates adaptive noise cancelling of cardiac interference and noise-floor adjustable detection thresholds, removes LM secondary to sleep disordered breathing within 5 sec of respiratory events, and is robust to transient artifacts. Furthermore, it provides PLM indices for sleep (PLMS) and wake plus periodicity index and other metrics. To validate the final S-PLMAD, experts visually scored 78 studies in normal sleepers and patients with restless legs syndrome, sleep disordered breathing, rapid eye movement sleep behavior disorder, narcolepsy-cataplexy, insomnia, and delayed sleep phase syndrome. PLM indices were highly correlated between expert, visually scored PLMS and automatic scorings (r² = 0.94 in WSC and r² = 0.94 in SSC). In conclusion, The S-PLMAD is a robust and high throughput PLM detector that functions well in controls and sleep disorder patients.</p></div

A rapid review method for extremely large corpora of literature: applications to the domains of modelling, simulation and management

While literature reviews with a large-scale scope are nowadays becoming a staple element of modern research practice, there are many challenges in taking on such an endeavour, yet little evidence of previous studies addressing these challenges exists. This paper introduces a practical and efficient review framework for extremely large corpora of literature, refined by five parallel implementations within a multi-disciplinary project aiming to map out the research and practice landscape of modelling, simulation, and management methods, spanning a variety of sectors of application where such methods have made a significant impact. Centred on searching and screening techniques along with the use of some emerging IT-assisted analytic and visualisation tools, the proposed framework consists of four key methodological elements to deal with the scale of the reviews, namely: (a) an incremental and iterative review structure, (b) a 3-stage screening phase including filtering, sampling and sifting, (c) use of visualisation tools, and (d) reference chasing (both forward and backward). Five parallel implementations of systematically conducted literature search and screening yielded a total initial search result of 146 087 papers, ultimately narrowed down to a final set of 1383 papers which was manageable within the limited time and other constraints of this research work

PLMS/h comparisons between automatic methods and manually scoring.

<p>The squared correlation coefficient (r²) between PLMS/h determined automatically versus manually is shown in the table for previously published detectors and our PLM calculator with and without the SNR+ option. PLM are classified according to AASM 2007 scoring criteria^a with adjustment to LM classification for our classifier as described in the text.</p><p>*RLS symptoms as defined in the text.</p>a<p>Iber C A-IS, Chesson A, Quan SF. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. Westchester, Ill: American Academy of Sleep Medicine, 2007.</p>b<p>Tauchmann NPT. Automatic Detection of Periodic Leg Movements. J Sleep Res 1996; 5(4): 273–5.</p>c<p>Wetter TC, Dirlich G, Streit J, Trenkwalder C, Schuld A, Pollmacher T. An automatic method for scoring leg movements in polygraphic sleep recordings and its validity in comparison to visual scoring. Sleep 2004; 27(2): 324–8.</p>d<p>Ferri R, Zucconi M, Manconi M, et al. Computer-assisted detection of nocturnal leg motor activity in patients with restless legs syndrome and periodic leg movements during sleep. Sleep 2005; 28(8): 998–1004.</p><p>PLMS/h comparisons between automatic methods and manually scoring.</p

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Automatically obtained PLM biomarkers in Wisconsin Sleep Cohort.

<p>PLM metrics are calculated from PLM classified using our detector (SNR+). PLM/h and periodicity indices are calculated for wake (PLMW/h) and sleep (PLMS/h). Patients not using CPAP and having more two hours of evaluation were grouped according to their apnea-hypopnea index (AHI). CPAP users are not excluded from the “All” category. An hour of the night effect is seen with PLM, occurring almost five times as frequently in the first half of the night compared to the second half (PLM night ratio), while leg movement activity occurs equally across the night in patients with respiratory difficulty during sleep. Note that the periodicity index is artificially affected by excluding intermittent wake and produces a statistically significant higher value in patients with AHI≤15 when examining sleep only.</p><p>Data are mean ± Standard Error Mean, or percentage. The number of subject used for calculations are shown in parentheses. Periodicity is the periodicity index. Count is the total number of individual PLM or LM counted per study. Night ratio is the ratio of events classified in the first half of each study divided by the number of events classified in the second half. Heart rate is the normalized cardiac change (beats per minute) time locked to PLM as described in the text. P-values are calculated from the student t-test with significance level of 0.05. Periodicity index, heart rate, and PLM ratio is only calculated in the presence of PLM. Night ratios are only calculated in cases where PLM or LM occur during both the first and second half of the study.</p><p>Automatically obtained PLM biomarkers in Wisconsin Sleep Cohort.</p

Automatically obtained PLM biomarkers in Stanford Sleep Cohort.

<p>PLM metrics are calculated from PLM classified using our detector (SNR+). PLM/h and periodicity indices are calculated for wake (PLMW/h) and sleep (PLMS/h). Patients having more two hours of evaluation were grouped according to sleep pathology as determined by formal medical diagnosis. The circadian effect is reversed in narcolepsy, with PLM more likely to occur during the second half of the sleep study, and less extreme in insomnia where PLM are only slightly more frequent (i.e. 33%) in the first half of the study.</p><p>Data are mean ± Standard Error Mean, or percentage. The number of subject used for calculations are shown in parentheses. Probabilities (p) are calculated using one-way analysis of variance between groups with a 0.05 significance level. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114565#pone-0114565-g004" target="_blank">Fig. 4</a> for description of terms.</p><p>Automatically obtained PLM biomarkers in Stanford Sleep Cohort.</p