Efficacy of Gutta‐Percha and Polytetrafluoroethylene Tape to Microbiologically Seal the Screw Access Channel of Different Prosthetic Implant Abutments
Bibliographic record
Abstract
PURPOSE: To evaluate in vitro, the efficacy of gutta-percha (GP) and polytetrafluoroethylene (PTFE) tape to act as a seal against the penetration of Escherichia coli through prosthetic implant abutments (PIA) with external hexagon (EH) and morse taper (MT) connections. MATERIALS AND METHODS: 120 sets of implant analogs with their respective PIA were used. 60 sets from each system were divided into four groups (n = 15): I - GP sealing; II - PTFE tape sealing; III - no sealing (NS) and IV - negative control. Microbiological challenge was performed by placing bacterial colonies inside the titanium PIA before screwing them into their respective analogs. Each set was immersed in 5 mL of BHI and incubated for 14 days with daily checks for contamination, according to the nutrient medium turbidity. RESULTS: The type of seal used influenced microbial penetration at all evaluation times (p < 0.05). Compared to GP, PTFE led to an increase in turbidity and its effectiveness against microbial penetration was equivalent to group NS. When the sealing efficacy of each group was compared in terms of time, Cochran's Q test revealed that in the two groups sealed with PTFE, there was a progressive increase in the number of turbid nutrient media (p < 0.0001), while in the group sealed with GP, this was only observed in the MT system (p = 0.0310). CONCLUSIONS: Sealing the PIA channel with GP was a significantly superior approach to sealing it with PTFE tape. PTFE tape or NS led to a progressive increase in microbiological leakage over time in PIA channels, however, this only occurred with GP in the MT system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".