Systematic Review of Approaches to Use of Neighborhood-Level Risk Factors With ClinicalData to Predict Clinical Risk and recomm

Despite a large body of literature investigating how the environment influences health outcomes, most published work to date includes only a limited subset of the rich clinical and environmental data that is available and does not address how these data might best be used to predict clinical risk or expected impact of clinical interventions. Identify existing approaches to inclusion of a broad set of neighborhood-level risk factors with clinical data to predict clinical risk and recommend interventions. A systematic review of scientific literature published and indexed in PubMed, Web of Science, Association of Computing Machinery (ACM) and SCOPUS from 2010 through October 2020 was performed.
To be included, articles had to include search terms related to Electronic Health Record (EHR) data Neighborhood-Level Risk Factors (NLRFs), and Machine Learning (ML) Methods. Citations of relevant articles were also reviewed for additional articles for inclusion. Articles were reviewed and coded by two independent reviewers to capture key information including data sources, linkage of EHR to NRLFs, methods, and results. Articles were assessed for quality using a modified Quality Assessment Tool for Systematic Reviews of Observational Studies (QATSO).
 A total of 334 articles were identified for abstract review. 36 articles were identified for full review with 19 articles included in the final analysis. All but two of the articles included socio-demographic data derived from the U.S. Census and we found great variability in sources of NLRFs beyond the Census. The majority or the articles (14 of 19) included broader clinical (e.g. medications, labs and co-morbidities) and demographic information about the individual from the EHR in addition to the clinical outcome variable.
Half of the articles (10) had a stated goal to predict the outcome(s) of interest. While results of the studies reinforced the correlative association of NLRFs to clinical outcomes, only one article found that adding NLRFs into a model with other data added predictive power with the remainder concluding either that NLRFs were of mixed value depending on the model and outcome or that NLRFs added no predictive power over other data in the model. Only one article scored high on the quality assessment with 13 scoring moderate and 4 scoring low.
In spite of growing interest in combining NLRFs with EHR data for clinical prediction, we found limited evidence that NLRFs improve predictive power in clinical risk models. We found these data and methods are being used in four ways. First, early approaches to include broad NLRFs to predict clinical risk primarily focused on dimension reduction for feature selection or as a data preparation step to input into regression analysis. Second, more recent work incorporates NLRFs into more advanced predictive models, such as Neural Networks, Random Forest, and Penalized Lasso to predict clinical outcomes or predict value of interventions.
Third, studies that test how inclusion of NLRFs predict clinical risk have shown mixed results regarding the value of these data over EHR or claims data alone and this review surfaced evidence of potential quality challenges and biases inherent to this approach. Finally, NLRFs were used with unsupervised learning to identify underlying patterns in patient populations to recommend targeted interventions. Further access to computable, high quality data is needed along with careful study design, including sub-group analysis, to better determine how these data and methods can be used to support decision making in a clinical setting.
Systematic Review of Approaches to Use of Neighborhood-Level Risk Factors With ClinicalData to Predict Clinical Risk and recomm

Rapid online teaching: movement of animal science courses online during COVID-19. Case study: pedagogical decisions in transitioning animal science courses online

Traditionally, earning a degree in animal science requires many face-to-face, hands-on courses; however, the COVID-19 pandemic created a situation in which traditional delivery of these courses may not be feasible as they provide a health risk to our students, teaching assistants, and instructors alike. This examination of two pedagogically different courses and how each was transitioned to an online format highlights the types of teaching decisions that are required to effectively teach animal science in an online format. The Farm Animal Production Systems lab was an animal handling and production practices lab, and although the transition to online delivery did not allow for students to participate in traditional hands-on development of skills, various resources were utilized that still achieved the development of animal handling concepts that will prepare students for later courses and work with live animals.

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Description: Goat anti Mouse IgG secondary antibody

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Description: Goat anti Mouse IgG secondary antibody

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Goat Anti Mouse IgG -Biotin

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488 Goat-anti-Mouse IgG

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546 Goat-anti-Mouse IgG

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Description: Goat polyclonal to mouse IgG

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Goat anti Mouse IgG (HRP)

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EUR 198
Description: Goat anti Mouse IgG secondary antibody  (HRP)

Goat anti Mouse IgG (FITC)

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EUR 186
Description: Goat anti Mouse IgG secondary antibody  (FITC)

Goat anti Mouse IgG (rhodamine)

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EUR 138
Description: Goat anti Mouse IgG secondary antibody  (rhodamine)

Goat Anti-Mouse IgG (Fc) IgG, unlabeled

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Goat Anti-Human IgG Antibody

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Rabbit Anti-Goat IgG Antibody

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Goat anti-Rat IgG Antibody

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Goat anti-Rat IgG Antibody

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Goat Anti-toxoplasma IgG antibody(Anti-toxIgG)

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Goat Anti Human IgG

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Rabbit anti Goat IgG

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EUR 343

goat Anti Rabbit IgG

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Goat Anti-human IgG

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EUR 283
Description: Biotinylated goat anti-human IgG enables detection of human IgG in enzyme immunoassays such as indirect ELISA.

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Rabbit anti Goat IgG

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Goat anti Human IgG

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EUR 149
Description: Affinity purified Goat anti Human IgG antibody

Donkey anti Goat IgG

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EUR 241
Description: Donkey anti Goat IgG secondary antibody

Donkey anti Goat IgG

40-DG10S 100 ml
EUR 133
Description: Donkey anti Goat IgG secondary antibody

Goat anti Rabbit IgG

40-GR30 100 ml
EUR 241
Description: Goat anti Rabbit IgG secondary antibody

Goat anti Rabbit IgG

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EUR 133
Description: Goat anti Rabbit IgG secondary antibody

Goat anti Rat IgG

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Description: Goat anti Rat IgG secondary antibody

Goat anti Cat IgG

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EUR 271
Description: Goat anti Cat IgG secondary antibody

Goat anti Cat IgG

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Description: Goat anti Cat IgG secondary antibody

Rabbit anti Goat IgG

40C-CB0536 1.5 mg
EUR 269
Description: Rabbit anti Goat IgG secondary antibody

Goat anti Rabbit IgG

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Description: Goat anti Rabbit IgG secondary antibody

Goat anti Monkey IgG

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Description: Goat anti Monkey IgG secondary antibody

Goat anti Rabbit IgG

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Description: Goat anti Rabbit IgG secondary antibody

Goat anti Rabbit IgG

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Goat anti Human IgG

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Description: Goat anti Human IgG secondary antibody

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Goat anti Rabbit IgG

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Description: Goat anti Rabbit IgG secondary antibody

Goat anti Monkey IgG

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Description: Goat anti Monkey IgG secondary antibody

Goat anti Human IgG

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Description: Goat anti Human IgG secondary antibody

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Description: Goat anti Human IgG secondary antibody

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Description: Goat anti Human IgG secondary antibody

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EUR 186
Description: Goat anti Human IgG secondary antibody

Goat anti Human IgG

41R-1030 1 mg
EUR 138
Description: Goat anti Human IgG secondary antibody

Goat anti Human IgG

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EUR 186
Description: Goat anti Human IgG secondary antibody

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Description: Goat anti Human IgG secondary antibody

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Description: Goat anti Human IgG secondary antibody

Goat anti Rabbit IgG

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Description: Goat anti Rabbit IgG secondary antibody

Donkey anti Goat IgG

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EUR 229
Description: Donkey anti Goat IgG secondary antibody

Goat anti-Mouse IgG (H&L) Antibody

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Goat Anti Mouse Igg (Fc) Polyclonal Antibody

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Goat Anti-Mouse IgG (H&L) Antibody

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Goat Anti-Mouse IgG (H+L) Antibody

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Goat Anti-Mouse IgG (Fc) Antibody (Biotin)

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Goat Anti-Mouse IgG (Fc) Antibody (FITC)

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In contrast, the Animal Science Laboratory Teaching Methods course remained consistent in format through the transition to online because students were still able to participate in discussion-based activities via Zoom meetings each week due to the small class size, which helped to maintain student engagement. However, the final teaching experience was modified to an alternative assignment. The alternate assignment included self-reflection and course evaluation that will help to improve both the Farm Animal Production Systems laboratory and the Animal Science Teaching Methods course in the future. Although COVID-19 has been a challenge that disrupted traditional courses, it has provided opportunities for a traditionally hands-on discipline, such as animal science, to more effectively engage students via an online platform.