Bacterial microleakage at the abutment‐implant interface, in vitro study
Bibliographic record
Abstract
INTRODUCTION: In implant rehabilitation, a microspace is created at the abutment-implant interface (AII). Previous research has shown that oral microbiome can proliferate in this microspace and affect periimplant tissues, causing inflammation in peri-implant tissues. Preventing microbial leakages through the AII is therefore an important goal in implantology. OBJECTIVE: To determine the presence of marginal bacterial microleakage at the AII according to the torque applied to the prosthetic implant in vitro. MATERIAL AND METHODS: Twenty-five Ticare Inhex internal conical implants (MG Mozo-Grau, Valladolid, España) were connected to a prosthetic abutment using torques of <10, 10, 20, 30, and 30 N and then sealed. The samples were submitted to cycles of occlusal loads and thermocycling, then one sample of each group was observed by micro TC, while the rest were mounted on devices according to the bacterial leakage model with Porphyromonas gingivalis. RESULTS: Bacterial leakage was observed only in the <10 and 10 N torque samples, and the same groups presented poor abutment/implant adjustment as determined by micro-CT. CONCLUSION: The different torques applied to the abutment-implant system condition the bacterial leakage at the implant interface. No microleakage was observed at 20 and 30 N.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".