scholarly journals Surrogate Variable

2020 ◽  
Author(s):  
Keyword(s):  
Surrogate Variable

Estimated effect of secondary screening for hip dislocation

2020 ◽  
Vol 105 (12) ◽  
pp. 1175-1179
Author(s):  
Daniel Wenger ◽  
Carl Johan Tiderius ◽  
Henrik Düppe
Keyword(s):  
Birth Cohort ◽  
Absolute Risk ◽  
Hip Dislocation ◽  
Absolute Risk Reduction ◽  
Age At Diagnosis ◽  
Hip Instability ◽  
Primary Screening ◽  
Surrogate Variable ◽  
Secondary Screening ◽  
Child Healthcare

ObjectivesTo quantify the effect of secondary screening for hip dislocations.DesignRetrospective analysis of hospital files from participants in a prospectively collected nationwide registry.SettingChild healthcare centres and orthopaedic departments in Sweden.ParticipantsOf 126 children with hip dislocation diagnosed later than 14 days age in the 2000–2009 birth cohort, 101 had complete data and were included in the study.InterventionsThe entire birth cohort was subject to clinical screening for hip instability at 6–8 weeks, 6 months and 10–12 months age. Children diagnosed through this screening were compared with children presenting due to symptoms, which was used as a surrogate variable representing a situation without secondary screening.Main outcome measuresAge at diagnosis and disease severity of late presenting hip dislocations.ResultsChildren diagnosed through secondary screening were 11 months younger (median: 47 weeks) compared with those presenting with symptoms (p<0.001). Children diagnosed through secondary screening had 11% risk of having a high (severe) dislocation, compared with 38% for those diagnosed due to symptoms; absolute risk reduction 27% (95% CI: 9.7% to 45%), relative risk 0.28 (95% CI: 0.11 to 0.70). Children presenting due to symptoms had OR 5.1 (95% CI: 1.7 to 15) of having a high dislocation, and OR 11 (95% CI: 4.1 to 31) of presenting at age 1 year or older, compared with the secondary screening group. The secondary screening was able to identify half of the children (55%, 95% CI: 45% to 66%) not diagnosed through primary screening.ConclusionsSecondary screening at child healthcare centres may have substantially lowered the age at diagnosis in half of all children with late presenting hip dislocation not diagnosed through primary screening, with the risk of having a high dislocation decreased almost to one-quarter in such cases.

scholarly journals Using Sensor Graphs for Monitoring the Effect on the Performance of the OTAGO Exercise Program in Older Adults

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 493
Author(s):  
Björn Friedrich ◽  
Carolin Lübbe ◽  
Enno-Edzard Steen ◽  
Jürgen Martin Bauer ◽  
Andreas Hein
Keyword(s):  
Older Adults ◽  
Pilot Study ◽  
Exercise Program ◽  
Exercise Programme ◽  
Control Group ◽  
Motion Sensors ◽  
Surrogate Variable ◽  
Physical Sensor ◽  
The Difference ◽  
Positive Effect

The OTAGO exercise programme is effective in decreasing the risk for falls of older adults. This research investigated if there is an indication that the OTAGO exercise programme has a positive effect on the capacity and as well as on the performance in mobility. We used the data of the 10-months observational OTAGO pilot study with 15 (m = 1, f = 14) (pre-)frail participants aged 84.60 y (SD: 5.57 y). Motion sensors were installed in the flats of the participants and used to monitor their activity as a surrogate variable for performance. We derived a weighted directed multigraph from the physical sensor network, subtracted the weights of one day from a baseline, and used the difference in percent to quantify the change in performance. Least squares was used to compute the overall progress of the intervention (n = 9) and the control group (n = 6). In accordance with previous studies, we found indication for a positive effect of the OTAGO program on the capacity in both groups. Moreover, we found indication that the OTAGO program reduces the decline in performance of older adults in daily living. However, it is too early to conclude causalities from our findings because the data was collected during a pilot study.

scholarly journals An improved and explicit surrogate variable analysis procedure by coefficient adjustment

Biometrika ◽  
2017 ◽  
Vol 104 (2) ◽  
pp. 303-316 ◽  
Author(s):  
Seunggeun Lee ◽  
Wei Sun ◽  
Fred A. Wright ◽  
Fei Zou
Keyword(s):  
Analysis Procedure ◽  
Surrogate Variable Analysis ◽  
Surrogate Variable ◽  
Variable Analysis

scholarly journals Worldwide patterns in species richness of falconiformes: analytical null models, geometric constraints, and the mid-domain effect

2004 ◽  
Vol 64 (2) ◽  
pp. 209-308
Author(s):  
T. F. L.V. B. Rangel ◽  
J. A. F. Diniz-filho
Keyword(s):  
Species Richness ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Empirical Support ◽  
Null Models ◽  
Geometric Constraints ◽  
Global Level ◽  
Spatial Gradients ◽  
Available Energy ◽  
Surrogate Variable

Recently, the hypothesis that the geographic distribution of species could be influenced by the shape of the domain edges, the so-called Mid-Domain Effect (MDE), has been included as one of the five credible hypotheses for explaining spatial gradients in species richness, despite all the unsuccessful current attempts to prove empirically the validity of MDE. We used data on spatial worldwide distributions of Falconiformes to evaluate the validity of MDE assumptions, incorporated into two different sorts of null models at a global level and separately across five domains/landmasses. Species richness values predicted by the null models of the MDE and those values predicted by Net Primary Productivity, a surrogate variable expressing the effect of available energy, were compared in order to evaluate which hypothesis better predicts the observed values. Our tests showed that MDE continues to lack empirical support, regardless of its current acceptability, and so, does not deserve to be classified as one possible explanation of species richness gradients.

scholarly journals Image-Based Surrogates of Socio-Economic Status in Urban Neighborhoods Using Deep Multiple Instance Learning

2018 ◽  
Vol 4 (11) ◽  
pp. 125 ◽  
Author(s):  
Christos Diou ◽  
Pantelis Lelekas ◽  
Anastasios Delopoulos
Keyword(s):  
Local Level ◽  
Multiple Instance Learning ◽  
Training Data ◽  
Percentage Error ◽  
Socioeconomic Indicators ◽  
Google Street View ◽  
Surrogate Variable ◽  
Car Detection ◽  
Street View ◽  
Surrogate Variables

(1) Background: Evidence-based policymaking requires data about the local population’s socioeconomic status (SES) at detailed geographical level, however, such information is often not available, or is too expensive to acquire. Researchers have proposed solutions to estimate SES indicators by analyzing Google Street View images, however, these methods are also resource-intensive, since they require large volumes of manually labeled training data. (2) Methods: We propose a methodology for automatically computing surrogate variables of SES indicators using street images of parked cars and deep multiple instance learning. Our approach does not require any manually created labels, apart from data already available by statistical authorities, while the entire pipeline for image acquisition, parked car detection, car classification, and surrogate variable computation is fully automated. The proposed surrogate variables are then used in linear regression models to estimate the target SES indicators. (3) Results: We implement and evaluate a model based on the proposed surrogate variable at 30 municipalities of varying SES in Greece. Our model has R 2 = 0 . 76 and a correlation coefficient of 0 . 874 with the true unemployment rate, while it achieves a mean absolute percentage error of 0 . 089 and mean absolute error of 1 . 87 on a held-out test set. Similar results are also obtained for other socioeconomic indicators, related to education level and occupational prestige. (4) Conclusions: The proposed methodology can be used to estimate SES indicators at the local level automatically, using images of parked cars detected via Google Street View, without the need for any manual labeling effort.

Cupulocrinid crinoids from the Middle Ordovician (Galena Group, Dunleith Formation) of northern Iowa and southern Minnesota

1992 ◽  
Vol 66 (1) ◽  
pp. 99-128 ◽  
Author(s):  
James C. Brower
Keyword(s):  
Niche Differentiation ◽  
Upper Ordovician ◽  
Average Width ◽  
Positive Allometry ◽  
Middle Ordovician ◽  
Surrogate Variable ◽  
Crown Volume ◽  
Food Particles ◽  
Anal Sac ◽  
Food Groove

Two cupulocrinids,Cupulocrinus crossmanin. sp. andPraecupulocrinus conjugans(Billings) n. gen., are known from the Middle Ordovician (Galena Group, Dunleith Formation) of northern Iowa and southern Minnesota. Various morphologic and ontogenetic features demonstrate thatPraecupulocrinusis more primitive thanCupulocrinus. The two species commonly occur together. In addition, both taxa coexisted at similar levels with stem lengths ranging from about 1.5 cm in juveniles to 15 cm in adults. Relatively complete growth sequences illustrate growth and variation and show how two related crinoids subdivided feeding niches. The crown volume provides a satisfactory surrogate variable for the size of the animal. The food-gathering system of the cupulocrinids is mainly augmented by the addition of new plates at the ends of the arms. The number of plates in the arms and the arm length exhibit positive allometry relative to crown volume, largely due to development of new branches at the arm tips. The food-gathering capacity equals the number of food-catching tube-feet multiplied by the average width of the food grooves. Food-gathering capacity is also positively allometric with respect to crown volume and the amount of tissue that must be supplied with food. Consequently, the ratio of food-gathering capacity:crown volume is either constant or declines slightly with increasing size and age. The food groove width increases throughout ontogeny so adult crinoids ate larger food particles than juveniles.Praecupulocrinus conjugans(Billings) n. gen. has more narrow food grooves thanCupulocrinus crossmanin. sp. of comparable size and age, which suggests niche differentiation based on food-particle size. The arm and tube-foot geometry indicates that both cupulocrinids utilized the same type of suspension feeding.The morphology of the anal sac and the lack of “patelloid” processes in the arms indicate thatCupulocrinus sepulchrumRamsbottom from the Upper Ordovician of Scotland belongs toDendrocrinus.

Determination of optimum target values for a tool wear process based on a surrogate variable

2015 ◽  
Vol 84 (9-12) ◽  
pp. 2491-2497 ◽  
Author(s):  
Cheol Eun Jeong ◽  
Hyuck Moo Kwon ◽  
Sung Hoon Hong ◽  
Taeho Park ◽  
Min Koo Lee
Keyword(s):  
Tool Wear ◽  
Wear Process ◽  
Surrogate Variable ◽  
Target Values

scholarly journals Image-Based Surrogates of Socio-Economic Status in Urban Neighborhoods Using Deep Multiple Instance Learning

2018 ◽  
Author(s):  
Christos Diou ◽  
Pantelis Lelekas ◽  
Anastasios Delopoulos
Keyword(s):  
Socioeconomic Status ◽  
Unemployment Rate ◽  
Local Level ◽  
Multiple Instance Learning ◽  
Training Data ◽  
Google Street View ◽  
Surrogate Variable ◽  
Car Detection ◽  
Street View ◽  
Surrogate Variables

1) Background: Evidence-based policymaking requires data about the local population's socioeconomic status (SES) at detailed geographical level, however such information is often not available, or is too expensive to acquire. Researchers have proposed solutions to estimate SES indicators by analyzing Google Street View images, however these methods are also resource-intensive, since they require large volumes of manually labeled training data. 2) Methods: We propose a methodology for automatically computing surrogate variables of SES indicators using street images of parked cars and deep multiple-instance learning. Our approach does not require any manually created labels, apart from data already available by statistical authorities, while the entire pipeline for image acquisition, parked car detection, car classification and surrogate variable computation is fully automated. The proposed surrogate variables are then used in linear regression models to estimate the target SES indicators. 3) Results: We implement and evaluate a model based on the proposed surrogate variable at 30 municipalities of varying SES in Greece. Our model has $R^2=0.76$ and correlation coefficient 0.874 with the true unemployment rate, while it achieves mean absolute percentage error 0.089 and mean absolute error 1.87 on a held-out test set. 4) Conclusions: The proposed methodology can be used to estimate socioeconomic status indicators such as unemployment rate at the local level automatically, using images of parked cars detected via Google Street View, without the need for any manual labeling effort.

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