Comparing the marginal leakage and retention of implant‐supported restorations cemented by four different dental cements
Bibliographic record
Abstract
BACKGROUND: Despite the wide use of implants in dentistry, there is insufficient information about the ideal cement for retention. PURPOSE: To determine the cement bond strength and marginal leakage of crown and partial denture cemented to implant abutments by four different types of cement. MATERIALS AND METHODS: Eighty-four direct abutments were divided into eight groups (n = 7). Fifty-six crown and bridge restorations were cemented using zinc phosphate (ZM), temporary cement (TM), resin-modified glass-ionomer cement (GM), and self-adhesive resin cement (RM). After cementation, thermal cycling and incubation in basic fuchsin dye was applied. The maximum load to failure, marginal leakage, and fracture modes were evaluated. RESULTS: The mean of retention strength for the bridges (874 N) was higher than the crown samples (705 N) (P = .005). The mean of retention strength for each cement group was ZM = 1298, RM = 1027, GM = 646, and TM = 187 N (P ≤ .0001). Marginal leakage was recorded in majority of the samples; the highest incidence was detected for ZM samples. The cement fracture was mostly adhesive in nature. CONCLUSION: Self-adhesive resin and resin-modified glass ionomer cement had better mechanical properties to retain implant supported restorations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".