Optimizing mandibular implant-overdentures based on 3D printing technologies and attachment systems
Bibliographic record
Abstract
Background: Since 2002, the use of two-implant mandibular overdentures (IOD) has been advocated as the minimum standard of care for treating mandibular edentulism. Despite favorable patient-reported outcomes and satisfactory clinical performance, the existing IODs designed for mandibular edentulism come with inherent limitations. The primary disadvantages of these dental prostheses include the loss of attachment retention due to wear over time, necessitating regular maintenance appointments. Another significant hurdle for IODs lies in their time-consuming and expensive fabrication process. Although conventional denture-making techniques have been well-established for over a century, recent developments have brought computer-aided design and manufacturing (CAD/CAM) options to the forefront for dental professionals.Objectives: The overall objective of this thesis has been to tackle the IODs’ limitations, specifically the loss of retention in attachment systems and the lengthy and costly fabrication processes, by exploring methods for the optimization of this type of dental prostheses.Methods: To address this objective, a combination of research methodologies was used. First, an in vitro study (manuscript I) was carried out to explore the retentive properties and structural integrity of two attachment systems (Novaloc and Locator attachments) for two-implant overdentures subjected to mechanical cycling representing masticatory forces for up to 12 months of use. Then, a scoping review (manuscript II), as well as a meta-analysis (manuscript III) were conducted to map the current literature regarding the CAD/CAM removable dental prosthesis. Through the fourth manuscript, a series of in vitro tests was performed to optimize the mechanical and surface properties of the 3D-printed denture base material through varying printing orientations and post-processing strategies before moving forward to the clinical trial phase. Lastly, in manuscript V, a protocol for the first cross-over mixed-methods randomized controlled trial (RCT) comparing CAD/CAM IODs with the conventional ones was developed.Results: The findings from the in vitro attachment study show that the Novaloc attachment system preserved retentive forces longevity relative to the Locator system, despite lower overall retentive force. The review and meta-analysis suggested that CAD/CAM dentures are comparable to conventional dentures regarding patient- and clinician-centered outcomes, yet they offer lower fabrication costs. In our set of experimental tests on 3D-printed denture base material, we found that both printing orientation and post-processing technique affect the mechanical and surface properties of these materials. Finally, we prepared a protocol for a cross-over mixed-methods RCT in comparing CAD/CAM IODs with the conventional ones, which has been approved by the Ethical Review Board at McGill University. The study is currently ongoing and began recruitment in January 2024.Conclusions: Our scoping review and meta-analysis indicated the need to conduct more well-designed high-quality RCTs regarding the CAD/CAM dentures, particularly IODs. Through our series of in vitro tests, we found an ideal scenario for the attachment system and processing parameter for 3D-printed denture base material to use in the clinical setting. Our results, if confirmed in our RCT, will have significant effect on clinical practice, by improving the tested procedure and showcasing an opportunity for greater access to oral health care for the edentulous population
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".