Retentive Strength of Metal Copings on Prefabricated Abutments with Five Different Cements
Bibliographic record
Abstract
BACKGROUND: Despite their wide use in implant dentistry, there is insufficient information concerning the retentive strength of cement-retained superstructures. PURPOSE: This study compared the retentive strength of metal copings on prefabricated abutments with five different luting cements. MATERIALS AND METHODS: Eight prefabricated abutments were placed on titanium screw implants torqued to 35 Ncm. Metal copings were cast with Au-Pt-Pd alloy (DeguDent Universal, Degussa, Hanau, Germany) using burnt-out plastic copings. Cements used were zinc oxide-eugenol-free temporary (ZO), zinc phosphate (ZP), glass ionomer (GI), resin-reinforced glass ionomer (RG), and composite resin (CR) cements. Retentive strength was measured with a universal testing machine. The means of each group were compared by one-way analysis of variance and Tukey-Kramer multiple-comparison intervals at a significance level of p < .05. RESULTS: The mean +/- SD retentive strength of the cements in Newtons was ZO 56 +/- 12 (Tukey group C), ZP 158 +/- 79 (Tukey group B), GI 132 +/- 29 (Tukey group B), RG 477 +/- 52 (Tukey group A), and CR 478 +/- 50 (Tukey group A). CONCLUSION: The retentive strength of metal copings on implant abutments is somewhat different from those of conventional cemented restorations on natural teeth. These differences may be influenced by differences in surface roughness and the height of the abutment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".