Severity: Warning
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Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
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Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
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Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
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Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
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Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
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Function: require_once
Introduction: Oligodontia is a rare dental developmental pathology that requires prolonged, complex and multidisciplinary treatment. Although bone augmentation is frequently required during a complete implant treatment of oligodontia. Therefore, we evaluated the ability to predict pre-implant surgery complexity based on age, number of missing teeth, and number of implants required to achieve implant-supported prosthetic rehabilitation.
Material And Methods: This retrospectively registered study included all patients who underwent surgical treatment for oligodontia in our Oral and Maxillofacial Surgery Department between January 2012 and May 2023. Demographic data, number and location of missing teeth, pre- and per-implant surgical procedures, and the number of planned implants were recorded. A quantitative variable called "complexity score of pre-implant surgery" was created. This 10-point score was calculated by adding one point for each preimplant surgical procedure registered. A simple linear regression was calculated to explain the number of targeted implants based on number of missing teeth. A multiple linear regression model was used to explain the complexity score of pre-implant surgery and age, number of missing teeth and number of targeted implants.
Results: 119 oligodontia patients were included in the study. The median number of tooth agenesis was 10. A total of 825 implants were placed, 14 (1.7%) of which failed. A significant regression equation was used (F(1,118) = 1098,338; p < 0.0001) to explain the number of targeted implants based on number of missing teeth, with a R of 0.903. A significant regression equation was found (F(3,116) = 107,229; p < 0.0001) to explain the complexity score of pre-implant surgery and age, number of missing teeth and number of targeted implants, with a R of 0.735.
Discussion: These results based on patient data indicate that age, number of missing teeth and number of targeted implants could reliably explain the complexity of pre-implant surgery.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11375859 | PMC |
http://dx.doi.org/10.1186/s12903-024-04827-0 | DOI Listing |
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