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Dr. Berg's New Body Type Guide

Description: The New Body Type Guide by Eric Berg, DC, is a major upgraded and improved version of his best selling book, The 7 Principles of Fat Burning. In his recent years, many new discoveries and observations prompted Dr. Berg to come out with a new version to bust through any slow metabolism. Dr. Berg will teach you how to take your results to a whole new level and get your body into super health state. Also added is several additional chapters on acupressure techniques to rid stress, pleasure food recipes that are healthy and how to stick to your plan no matter what comes up. But the major change is in what you are going to be eating. Forget about cravings, blood sugar imbalances and the numerous continued problems people have when they struggle to lose weight. This is your personal guild to customize your results based on your body type - let the adventure begin!

Dr. Berg's New Body Type Guide

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Ask students to clip magazine ads and fashion spreads of very thin or pumped-up models. Discuss whether these are attractive body types in real life, and if they are factors in being popular and happy.

The task of representing clinical guidelines in a machine-comprehensible format is complicated and has many different aspects. In the background section of the paper we summarize different approaches and studies in this field. These approaches often successfully solve a subset of the problems concerning guideline specification but typically do not address the full scope of these problems. The heart of the problem of creating and maintaining a scalable repository of formal medical knowledge is that, on the one hand, clinicians cannot and need not program in formal specification languages. On the other hand, computer programmers and knowledge engineers do not completely understand the semantics of medical knowledge and procedures. For both types of experts, time is limited and expensive.

In addition, another issue that requires special care when representing the knowledge embodied by clinical guidelines is that clinical guidelines include both procedural knowledge, such as workflows, as well as declarative knowledge, such as definition of clinically meaningful abstract terms and temporal patterns. Each type of knowledge requires a unique methodology and set of tools for its acquisition and specification, but both need to be integrated within the same, well-encapsulated guideline, while still supporting the option for reuse of the knowledge by future guidelines.

The documentation ontology includes documentary knowledge roles that are common to all guideline ontologies. The guideline's title, authors and semantic classification indices are examples of common elements. The documentation ontology distinguishes between source (free-text) guidelines and hybrid (structured at one or more levels) guidelines and provides different documentation elements for each of these guideline types. Source guidelines are stored as free-text (HTML) documents while hybrid guidelines are the products of the specification process. The knowledge roles of the documentation ontology were created according to knowledge roles existing in other guideline ontologies, for example, knowledge-roles describing the guideline's identity (e.g., title, date of publication, date of last revision); knowledge roles describing the guideline developers (e.g., developer name, committee name); and knowledge roles describing the guideline quality (e.g. strength of recommendation, level of evidence). A detailed description of the knowledge roles existing in several guideline ontologies can be found at [34], where the GEM ontology is described in detail and compared to other ontologies. Although DeGeL's documentation ontology includes most of GEM's documentary knowledge roles, it is important to mention that the ontology can be easily extended and the change will be immediately reflected in the guidelines library (i.e., all existing and new guidelines will be extended with elements to retain the new knowledge roles).

The specification meta-ontology defines multiple target specification ontologies (e.g. GLIF, Asbru) that can be used for guideline representation. It enables knowledge engineers to structure the guideline ontology (i.e., when adding a new ontology and when maintaining an existing one). The meta-ontology makes the following assumptions: the specification ontology consists of a hierarchical structure of plans and sub-plans as is common in all major ontologies (e.g., Asbru, GLIF, Prodigy, Proforma and others); several action and plan types exist, such as "medication" or "procedure," which are also common to all ontologies; and each guideline (which is composed from multiple plans and sub-plans) can relate to multiple declarative knowledge elements that describe the medical concepts.

As DeGeL supports storing and retrieving guidelines from multiple ontologies, it uses several standards to represent the specification languages. All entities within DeGeL, such as documentation ontologies, specification ontologies and guidelines are stored within a central database using a relational schema. The schema allows the library administrators to define a hierarchical structure for each of the ontologies. This hierarchical structure is used to represent the ontology's hierarchy of knowledge-roles. For example, the Asbru ontology includes hierarchy nodes for the guideline's intentions, plan-body, effect and conditions. The conditions node includes sub-nodes for filter, setup, complete, abort and other conditions. When a new guideline is created the schema stores data to describe each of its plans, which can have details according to all knowledge roles at multiple representation levels. The internal structure of the data that is stored in each knowledge role is defined by the Semi-Formal and Formal schemas of each specification language. Each specification language is defined by an XML schema that is used by the knowledge specification tool to validate the content of the knowledge roles.

The hierarchical plan builder in Gesher that the expert physicians use for specifying the procedural aspects of the guideline. In this case the "Management and Treatment" plan, of the PET guideline, composed of evaluating the patient's state (severe or mild preeclampsia) and applying the matching treatment. The user selects plans from different types (frame 1) and adds them to the hierarchical flow chart (frame2). For each plan, several properties can be defined (frame 3); for composite plans the procedural aspect are specified (frame 4). The sub-plan hierarchy is displayed in a tree-view display (frame 5). In this phase the expert also defines a list of declarative concepts that will be further specified in the following phases (frame 6).

The knowledge map tool provides the ability to organize and describe the concepts within a specific guideline, at a level of detail that will later allow creating the formal representation. Each concept in the guideline is described by several attributes that are available in the graphical interface. These attributes include a textual description; a description of the type of concepts; a description of the possible values; and a definition of temporal aspects such as the period during which a certain measurement of the concept value is valid in the context of applying this guideline (i.e., for how long is a specific measurement valid).

The knowledge map interface was functional and usable in supporting the specification of the semi-formal representation. Regarding completeness and correctness, the quality of the resultant specifications was very high. A mean time of 5 hours and 42 minutes, including the one-time training session, for completing the task of specifying all concepts within a relatively complex guideline such as the PET, was very reasonable, and provides another indication as to the feasibility of performing this task using the knowledge map. The usability scores given to the interface were high as well. An interesting fact is that the collaborative teams and the knowledge engineers produced a specification within a considerably shorter time that was significantly more complete than the physicians. This fact may be possibly due to several factors: the experience of the knowledge engineer in completing such tasks in complex computer environments; the more technical and organized approach of the engineers in solving these types of problems; and the fact that the physicians, given their external time constraints, had to complete the task in multiple short sessions, which might have had a cognitive effect (at each session, they needed to remember their work from previous sessions and continue from this stage). 041b061a72


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