Cementing an Implant Crown: A Novel Measurement System Using Computational Fluid Dynamics Approach
Bibliographic record
Abstract
BACKGROUND: Cementing restorations to implants is a widely used clinical procedure. Little is known about the dynamics of this process. Using a systems approach and advanced computing software modeling this can be investigated virtually. These models require validation against real-life models. PURPOSE: The study aims to consider the system effect of a crown, abutment, and cement flow under different conditions and comparing real physical models to virtual computer simulations. MATERIALS AND METHODS: A physical model of implant abutments and crowns provided three groups according to abutment screw access modification (n = 9): open (OA), closed (CA), and internal vented (IVA) abutment groups. Crowns were cemented using standardized amounts and site application. Proportion of cement retained within the crown-abutment system was recorded and compared. Differences among groups were identified using analysis of variance (ANOVA) with Tukey's post hoc test (α ≤ 0.05). Three-dimensional multiphysics numerical stimulation software (STAR-CCM+, CD-adapco) with computational fluid dynamics (CFD) approach was applied to a virtual model system of a scanned abutment and crown system. Three-dimensional real-time model simulations of cement and air displacement were produced, evaluating cement application site, speed of crown seating, and abutment modifications. RESULTS: Statistically significant differences in cement retained within the system (p < 0.01) were found among the IVA > OA > OCA abutment groups. The CFD virtual simulations followed this trend. Site application and speed of seating also affected cement extrusion and cement marginal infill. Fast crown seating and occlusal cement site application produced air incorporation at the margins. CONCLUSIONS: The CFD approach provides a convenient way to evaluate crown-cement-implant abutment systems with respect to cement flow. Preliminary evaluation indicates that the results achieved follow those of a physical actual cement-retained crown-implant abutment study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".