Decontamination of titanium implants using physical methods
Bibliographic record
Abstract
OBJECTIVES: Current decontamination methods of titanium (Ti) implant present limited success in achieving predictable re-osseointegration. We hypothesized that even though these techniques could be useful in elimination of bacteria, they might be unsuccessful in removing organic contaminants and restoring the original surface composition. The aim of this study was to assess the effect of four decontamination methods on the surface chemistry and bacterial load of biofilm-contaminated implant surfaces in order to improve implant surface decontamination. MATERIAL AND METHODS: The ability of clinically available methods such as metal and plastic curettes, Ti brushes and Er: YAG laser to decontaminate Ti implant surfaces was assessed. Surface morphology, chemical composition and properties of machined Ti discs (Ø 5.0 and 1.0 mm thick) were analysed before and after oral biofilm contamination using scanning electron microscope and X-ray photoelectron spectroscopy. The presence and viability of bacteria were evaluated with live-dead assays. RESULTS: Biofilm contamination created an organic layer rich in hydrocarbons and bacteria that covered entirely the Ti surfaces. This organic layer has tightly adhered to Ti surfaces and could not be completely removed with any of the methods assessed. Ti brushes achieved greater elimination of organic contaminants and bacteria than curettes and Er: YAG laser; however, none of them was able to restore the original surface chemistry. Alternatively, Er: YAG laser-treated surfaces showed the lowest live-to-dead bacterial ratio. CONCLUSIONS: Ti brushes were more effective than curettes (metal or plastic) and Er: YAG laser in decontaminating Ti implant surfaces, although none of these techniques was able to completely eliminate surface contamination. Er: YAG laser was more effective than curettes and Ti brushes in killing the biofilm bacteria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".