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A Suggested Prediction Model for Fixed- and Removable Partial Dentures
Umeå University, Faculty of Medicine, Department of Odontology.
2015 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

When replacing lost teeth, as well as other oral tissues, with artificial prostheses, the dentist often rely solely on clinical experience to determine the prognosis for a particular prosthesis. Even though there are numerous reports on success- and failure rates for different types of prostheses, today there is still no report of a model that can provide guidance on whether the prognosis for a given treatment is good or poor. This may be particularly troublesome for professionals with limited experience in the field of oral prosthetics.

In an attempt to alleviate this lack of a guiding tool in clinical prosthetic dentistry, a model was constructed based upon possible risk indicators and risk factors that were considered relevant, and that could prove to be important for the long-term outcome of the treatment, i.e. a prosthetic prognosis. By using this model together with acquired clinical skills, contrary to rely on experience alone, the aim was to increase the probability of making more accurate predictions on prosthetic treatments.

After an initial interview with a specialist in oral prosthetics, an electronic literature search was conducted on PubMed for articles on possible risk indicators and risk factors for prosthetic treatments. With the information retrieved, a prediction-model was constructed.

In this model; caries, periodontitis, endodontic treatment, prosthetic material, anatomical conditions, prosthetic design, patient adaption/cooperation, patient motivation, as well as general health were considered to be factors with a possible impact on prosthetic treatment outcome and prognosis.



Place, publisher, year, edition, pages
National Category
Medical and Health Sciences
URN: urn:nbn:se:umu:diva-114876OAI: diva2:898666
Available from: 2016-01-29 Created: 2016-01-29 Last updated: 2016-02-08Bibliographically approved

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