1. Needs for re-intervention on restored teeth in adults: a practice-based study
- Author
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Marie Tohmé, François Gueyffier, Valentin Garyga, Alexandra David, Charlène Chevalier, Franck Decup, Emmanuelle Dantony, Patrice Nony, Delphine Maucort-Boulch, and Brigitte Grosgogeat
- Subjects
Dental Restoration Failure ,medicine.medical_specialty ,business.industry ,Cross-sectional study ,Context (language use) ,Patient satisfaction ,Private practice ,Rating scale ,Physical therapy ,Medicine ,Observational study ,Risk factor ,business ,General Dentistry - Abstract
Evaluate the need for re-intervention on dental coronal restorations in adults seen in a network of general dental practitioners (ReCOL). This observational, cross-sectional, multicenter study involved 40 practitioners and 400 patients. Coronal restoration failures (needing re-intervention for unsatisfactory outcomes) were assessed with a simplified rating scale of seven criteria from the FDI World Dental Federation. The oral health status, the risk factors, and Oral Health Impact Profile-14 were also examined. Previous restoration characteristics (extent, technique, material) were analyzed according to the need for re-intervention (yes/no), the age group, and the risk profile. Qualitative variables were compared between “re-intervention” and “no re-intervention” group using Fisher exact test. The need for re-intervention was estimated at 74% (95% CI: 70; 79); it increased with age (49 to 90%), unfavorable risk profile (82 vs. 62%), and extent of the filling (32, 39, 44, and 44% on 1, 2, 3 surfaces, and crowns, respectively). More posterior than anterior teeth were restored (median per patient: 6 vs. 1) or needed re-intervention (median per patient: 1 vs. 0). The needs for re-intervention in adults are still high within a context of ever-changing materials and techniques, simplified and rationalized decision-makings, and demands for patient involvement. Meeting these needs requires the following: (i) consensus definitions and assessment methods for “failure” and (ii) reliable feedbacks on materials, procedures, and satisfaction. Building large and detailed databases fed by networks of motivated practitioners will help analyzing complex success/failure data by artificial intelligence and guiding treatment and research.
- Published
- 2021
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