Clinical Factors Influencing Removal of the Cement Excess in Implant‐Supported Restorations
Bibliographic record
Abstract
BACKGROUND: The depth of the cementation margin has an influence on the amount of cement remnants around implants. However, the role of other clinical factors is still not clarified. PURPOSE: The aim of the study was to evaluate the correlation between undetected cement and (i) location of the implant, (ii) implant diameter, and (iii) undercut. MATERIALS AND METHODS: Sixty-five patients were treated with single metal-ceramic restorations on implants. The undercut between the restoration and the tissue was measured. After cementation, the restoration-abutment unit was unscrewed. All quadrants of the specimens were photographed and analyzed. The ratio between total restoration area/peri-implant tissue area and area of cement remnants was calculated in pixels. Significance was set to 0.05. RESULTS: Sixty-five metal-ceramic restorations were placed on 65 implants (39 molars, 22 premolars, 4 anteriors; 21 implants had a diameter of 3.5 mm, 34 of 4.0 mm, 10 of 5.0 mm). An undercut of 1 mm was found in 118 sites, 2 mm in 96 sites, and 3 mm in 46. The percentages of soft tissue and restoration, respectively, covered by cement were as follows: molars 4% and 7%; premolars 3.8% and 7.3%; anteriors 3% and 3.4%; 3.5 mm diameter 3.3% and 7.4%; 4.0 mm 7.7% and 7.7%; 5.0 mm 3.9% and 2.1%; 1-mm undercut 3.5% and 5.4%; 2-mm 4% and 8.1%; 3-mm 4.8% and 8.4%. The relationship between amount of cement remnants and implant location was insignificant (p > 0.05) for both soft tissue and the specimen, but significant relationships with amount of cement remnants were found for diameter (p = 0.026 for soft tissue, p = 0.600 for specimen) and undercut (p = 0.004 for soft tissue, p = 0.046 for specimen). CONCLUSION: If cemented crown restoration is desired, undercuts should be reduced to a minimum for better removal of cement excess, irrespective of the diameter and location of the implants in the mouth.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".