Impact of dental cement on the peri‐implant biofilm‐microbial comparison of two different cements in an in vivo observational study
Bibliographic record
Abstract
BACKGROUND: The type of cement used in cemented fixed implant-supported restorations influences formation of undetected excess cement and composition of the peri-implant biofilm. Excess cement and dysbiosis of the biofilm involve the risk of peri-implant inflammation. PURPOSE: The aim of the study was to investigate the impact of two different cements on the peri-implant biofilm and inflammation. MATERIALS AND METHODS: In an observational study, the suprastructures of 34 patients with cemented fixed implant-supported restorations were revised. In 20 patients, a methacrylate cement (Premier Implant cement [PIC]) and in 14 patients, a zinc oxide eugenol cement (Temp Bond [TB]) were used. After revision, TB was used for recementation. During revision and follow-up after 1 year, microbial samples were obtained. RESULTS: Excess cement was found in 12 (60%) of the 20 patients with PIC. Suppuration was observed in two (25%) implants with PIC without excess cement (PIC-) and in all 12 (100%) implants with PIC and excess cement (PIC+). Implants cemented with TB had neither excess cement nor suppuration. The taxonomic analysis of the microbial samples revealed an accumulation of periodontal pathogens in the PIC patients independent of the presence of excess cement. Significantly, fewer oral pathogens occurred in patients with TB compared to patients with PIC. TB was used in all cases (PIC and TB) for recementation. In the follow-up check, suppuration was not found around any of the implants with PIC-, only around one implant with PIC+ and around one implant with TB. Bacterial species associated with severe periodontal infections that were abundant in PIC- and PIC+ samples before the revision were reduced after 1 year to levels found in the TB samples. CONCLUSIONS: The revision and recementation with TB had a positive effect on the peri-implant biofilm in cases with PIC. The cementation of suprastructures on implants with TB is an alternative method to 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".