Bacterial Colonization of the <scp>I</scp>mplant‐<scp>A</scp>butment <scp>I</scp>nterface (<scp>IAI</scp>) of Dental Implants with a Sloped Marginal Design: An <i>in‐vitro</i> Study
Bibliographic record
Abstract
PURPOSE: The aim of this study is to utilize an in vitro dynamic loading model to assess the potential risk of bacterial invasion into the Implant Abutment Interface (IAI) microgap of dental implants with sloped marginal design. MATERIALS AND METHODS: Forty implants were divided into two groups (n = 20 per group) based on implant marginal design. Group 1 was comprised of implants with Morse-taper connection and conventional marginal design that connected to titanium abutments. Group 2 was comprised of implants with Morse-taper connection and sloped marginal design that connected to titanium abutments. The specimens were immersed in a bacterial solution of E. coli and loaded with 500,000 cycles of 160N using a chewing simulator. Following disconnection of fixtures and abutments, microbial samples were taken from the threaded portion of the abutment, plated and cultured under appropriate conditions. RESULTS: Ten out of twenty implants of Group 1 and eight out of twenty implants of Group 2 had IAI microgaps colonized by E. Coli. There was not a statistically significant difference in the mean number of E. Coli CFU detected between implants of Group 1 (mean 19.2, SD 23.6) and Group 2 (mean 12.5, SD18.9) (p > .05). CONCLUSIONS: The present study demonstrated that implants with a sloped marginal design exhibited similar risk for bacterial invasion into the IAI microgap under in vitro dynamic loading conditions compared to implants with conventional marginal design.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".