Medical NBDE-II : Dental Board Exams Part II Exam
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Test Number : NBDE-II
Test Name : Dental Board Exams Part II
Vendor Name : Medical
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Format | NBDE-II Course Contents | NBDE-II Course Outline | NBDE-II test
Syllabus | NBDE-II test
The NBDE Part II is a comprehensive examination consisting of 500 items. For each discipline, approximately
80% of the items are stand-alone, while approximately 20% are interdisciplinary and case-based. A case consists
of a patient scenario, patient history, and a set of discipline-based items relevant to the scenario. NBDE items
are developed by test construction teams composed of subject-matter experts in accordance with examination
specifications approved by the JCNDE.
The Universal/National System for tooth notation that has been adopted by the American Dental Association is
used on all National Board Examinations. This system is a sequential tooth numbering system, designating the
permanent dentition (numbers 1-32), and the primary dentition (letters A-T).
In the June 2018 issue of the Journal of Periodontology the American Academy of Periodontology (AAP) published
updates to its Classification of Periodontal and Peri-Implant Diseases and Conditions. Candidates completing
examinations published through the Department of Testing Servicesincluding those overseen by the Joint
Commission on National Dental Examinations or by the American Dental Associationcontinue to be based on
the prior (i.e., 1999) AAP disease classification. Candidates will be notified when examinations are updated to
reflect the new terminology.
Discipline-Based Component (400 items)
items that comprise the discipline-based component are derived from the following disciplines:
2. Operative Dentistry
3. Oral and Maxillofacial Surgery/ Pain Control
4. Oral Diagnosis
5. Orthodontics/Pediatric Dentistry
6. Patient Management
Case-Based Component (100 items)
The case-based component of the NBDE Part II presents items dealing with genuine
patients. The patient cases
are developed to include the following approximate distribution: adult patients 70%; child patients 30%. A
minimum of 15% of case-based test
questions will address the medical management of compromised adults
and children. A compromised patient is defined as a person whose health status requires modification of standard
treatment. Each case presentation in the examination consists of:
1. synopsis of a patients health and social histories,
2. patient dental charting,
3. diagnostic radiographs, and
4. clinical photographs of the patient (when necessary).
Each case contains from 10 to 15 questions about various aspects of the patients dental care. These questions--
totaling 100 across all cases-- might derive from any of the biomedical sciences and clinical disciplines, including
Patient Management. The proportion stemming from any particular discipline depends upon the nature of the
case itself. For example, the case of an elderly adult might be based upon Maxillofacial Surgery/Pain Control,
Prosthodontics, and Operative Dentistry, whereas a childs case might derive from Orthodontics, Pediatric
Dentistry, and Patient Management. In responding to case-based items, the candidate must:
1. interpret the findings and information provided.
2. identify problems and make diagnoses.
3. select materials, technique, and armamentarium.
4. apply treatment.
5. evaluate progress and complications.
6. establish procedures for prevention and maintenance.
Endodontics* (36 items)
Case Clinical Diagnosis, Case Selection, Treatment Planning, and Patient Management
Basic Endodontic Treatment Procedures
Adjunctive Endodontic Therapy
Operative Dentistry (44 items)
Examination, Diagnosis, & Treatment Planning
General Operative Procedures
Preparation of Cavities
Restoration of Prepared Cavities
Oral And Maxillofacial Surgery/Pain Control (52 items)
Anxiety and Pain Control
Medical Assessment and Emergency Care
Orthodontics/Pediatric Dentistry (44 items)
Individual Tooth Pathology
Supporting Tissue Pathology
Patient Management (53 items)
Communication and Interpersonal Skills
Anxiety and Pain Control
Health Behavior Change
Disabled and Medically Compromised
Prevention of Oral Diseases
Evaluation of Dental Literature
Materials and Equipment Safety
Prevention and Maintenance
Pharmacology (35 items)
Central Nervous System
Antihistamines and Autocoids
Prosthodontics (48 items)
Complete and Removable Partial Denture Prosthodontics
Fixed Partial Prosthodontics
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Medical Part learn
docs and healthcare people might also sooner or later use a laptop discovering mannequin, called deep learning, to e book their medication decisions for lung cancer patients, according to a team of Penn State great Valley researchers.
In a examine, the researchers document that they developed a deep gaining knowledge of model that, in certain circumstances, changed into greater than seventy one% accurate in predicting survival expectancy of lung cancer sufferers, tremendously superior than traditional laptop researching models that the crew tested. The other computer gaining knowledge of fashions the crew established had a couple of 61% accuracy rate.
assistance on a affected person's survival expectancy could support book medical doctors and caregivers in making improved decisions on the usage of medicines, allocating elements and choosing the depth of look after patients, in line with Youakim Badr, associate professor of information analytics.
this is a high-performance equipment it is tremendously correct and is aimed toward helping docs make these vital choices about proposing care to their sufferers. Of route, this device can not be used as a substitute for a physician in making selections on lung cancer remedies."
Youakim Badr, affiliate Professor of information Analytics, Penn State
in keeping with Robin G. Qiu, professor of suggestions science and engineering and an affiliate of the Institute for Computational and records Sciences, the mannequin can analyze a big volume of facts -; usually known as points in computing device gaining knowledge of -; that describe the sufferers and the disorder to consider how a mix of components affect lung cancer survival periods. elements can include assistance corresponding to kinds of cancer, size of tumors, the velocity of tumor increase, and demographic information.
Deep studying may be uniquely suited to address lung cancer prognosis since the mannequin can provide the amazing analysis critical in melanoma research, in accordance with the researchers, who document their findings in foreign Journal of medical Informatics. Deep researching is a sort of desktop learning it is in line with synthetic neural networks, which can be frequently modeled on how the human brain's own neural community functions.
In deep discovering, although, builders observe a complicated constitution of multiple layers of those artificial neurons, which is why the model is called "deep." The learning aspect of deep researching comes from how the system learns from connections between statistics and labels, observed Badr.
"Deep discovering is a computing device-studying algorithm that makes associations between the data, itself, and the labels that they use to explain the records examples," spoke of Badr. "by way of making these associations, it learns from the records."
Qiu added that deep getting to know's structure offers several advantages for a lot of information science initiatives, principally when confronted with facts units which have a huge number of records -; during this case, sufferers -; as well as a big variety of elements.
"It improves performance incredibly," spoke of Qiu. "In deep studying they can go deeper, which is why they call it that. In typical machine researching, you have got an easy structure of layers of neural networks. In each layer, you've got a bunch of cells. In deep learning, there are many layers of these cells that can be architected into an advanced constitution to function better characteristic transformation and extraction, which gives you the ability to further increase the accuracy of any mannequin."
in the future, the researchers would like to Excellerate the mannequin and verify its potential to research other forms of cancers and medical circumstances.
"The accuracy expense is first rate, but it's now not excellent, so a part of their future work is to enrich the model," talked about Qiu.
To additional enhance their deep getting to know mannequin, the researchers would additionally should connect with domain experts, who're americans who have certain expertise. in this case, the researchers would want to join with certified
on specific cancers and clinical conditions.
"In lots of instances, they might no longer be aware of a lot of features that may still go into the mannequin," mentioned Qiu. "but, via collaborating with domain consultants, they could aid us compile crucial points about sufferers that they could not be aware of and that could extra enhance the model."
The researchers analyzed records from the Surveillance, Epidemiology, and conclusion results (SEER) software. The SEER dataset is one of the largest and most comprehensive databases on the early prognosis guidance for melanoma sufferers in the united states, in keeping with Shreyesh Doppalapudi, a graduate-student analysis assistant and first author of the paper. The program's melanoma registries cowl nearly 35% of U.S. cancer patients.
"one of the vital definitely good issues about this records is that it covers a big component of the inhabitants and it's truly diverse," pointed out Doppalapudi. "one other decent thing is that it covers loads of distinct elements, which that you would be able to use for numerous functions. This becomes very constructive, exceptionally when the usage of laptop discovering methods."
Doppalapudi brought that the crew in comparison several deep researching procedures, including synthetic neural networks, convolutional neural networks and recurrent neural networks, to natural laptop researching fashions. The deep studying approaches performed a whole lot enhanced than the natural machine gaining knowledge of strategies, he stated.
Deep getting to know structure is better acceptable to processing such significant, diverse datasets, such because the SEER program, in keeping with Doppalapudi. engaged on these styles of datasets requires mighty computational capacity. in this analyze, the researchers relied on ICDS's Roar supercomputer.
With about 800,000 to 900,000 entries in the SEER dataset, the researchers pointed out that manually finding these associations within the records with an entire crew of scientific researchers would be extremely complex with out tips from laptop learning.
"If it were only three fields i might say it will be unattainable -; and they had about a hundred and fifty fields," observed Doppalapudi. "knowing all of these distinct fields and then analyzing and researching from that counsel, could be unattainable."
Doppalapudi, S., et al. (2020) Lung melanoma survival duration prediction and realizing: Deep getting to know methods. foreign Journal of clinical Informatics. doi.org/10.1016/j.ijmedinf.2020.104371.