How can excess residual cement be reduced in implant‐supported restorations?: An in vitro study
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate the effect of different cements and cementation techniques used in implant-supported restorations and the application of various vent modifications and extraoral replica techniques on the amount of overflowing cement in cemented systems. MATERIALS AND METHODS: In this study, three different abutment designs were used as fully closed, occlusal vented, and occlusal + proximal vented. An extraoral replica was produced by milling the CAD/CAM ceramic block. The number of groups with and without replicas was determined as six (n = 10). For the cementation procedures, three different cements were tested: dual-cure resin, eugenol-free zinc oxide, and polycarboxylate cements. Cobalt-chromium superstructures to be cemented to the implant analog-abutment complex were produced by direct metal laser sintering method. Twenty-four hours after the cementation process, residual cement were measered with Micro-CT. In comparisons between groups, ANOVA test was used for normally distributed variables and Kruskall-Wallis H test was used for non-normally distributed variables at a significance level of p < 0.05. RESULTS: The difference in residual cement volumes between the groups in terms of both cementation techniques (whether or not to use an extraoral replica and different vent desings) and cement types was found to be statistically significant (p < 0.05). There was significantly less residual cement in all groups that used extraoral replicas than those that did not. As for the cement types, the most residual cement occurred in the resin cement. CONCLUSION: The use of extraoral replicas and vent designs on the abutment significantly reduces the amount of residual cement. Regardless of the cementation technique, the type of cement used affects the amount of excess cement. CLINICAL RELEVANCE: To reduce residual cement, both the type of cement and the cementation technique used must be considered.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".