Predictors of Excess Cement and Tissue Response to Fixed Implant‐Supported Dentures after Cementation
Bibliographic record
Abstract
BACKGROUND: The cementation of fixed implant-supported restorations involves the risk of excess cement remaining in the peri-implant tissue that may cause a peri-implant tissue response with attachment loss. PURPOSE: The aim was to study the peri-implant tissue response after cementation and to detect potential predictors of excess cement. MATERIAL AND METHODS: Clinical complications after cementation in several index cases led to a recall of all patients treated with a special methacrylate cement (one hundred five patients with one hundred eighty-eight implants) and systematic reevaluation of 71 patients (68%) with one hundred twenty-six implants (67%). In all cases, suprastructures including abutments were removed, and findings were documented. RESULTS: Implant diameter was significantly associated with the frequency of excess cement. Implant location or system had no significant effect. Excess cement in turn was associated with bleeding on probing, suppuration, and peri-implant attachment loss. In the absence of excess cement 58.8% of implants had no peri-implant attachment loss versus 37.3% when excess cement was present. With increasing retention time of the methacrylate cement, more peri-implant attachment loss was detected. However, the latter association was not significant. CONCLUSION: Larger diameters are significantly associated with excess cement in peri-implant tissue. Consequences of excess cement may be increased bleeding on probing, suppuration, and possibly peri-implant attachment loss.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".