Effects of air abrasive decontamination on titanium surfaces: A systematic review of in vitro studies
Bibliographic record
Abstract
BACKGROUND: Air abrasion (AA) is one of the decontamination methods that have demonstrated promising results in treating peri-implant diseases. PURPOSE: This systematic review aimed at evaluating the in vitro effect of AA on surface change, cleaning efficacy, and biocompatibility of titanium surfaces and at comparing it with other decontamination methods. MATERIALS AND METHODS: A comprehensive search was conducted up to April 2018 using PubMed, Scopus, and Google Scholar databases to identify studies on the decontamination effect of AA. All types of titanium surfaces, abrasive powders, contaminated surfaces, and measuring methods were included. RESULTS: Overall, 1502 articles were identified. After screening the titles and abstracts, and carefully reading the full-texts, 48 articles published between 1989 and 2018 were selected. AA was considered almost safe, particularly for the nonmodified surfaces. Nevertheless, harder powders such as sodium bicarbonate tended to damage the surface more than glycine. AA resulted in surface change similar to plastic curettes and Er: YAG lasers. Regarding the cleaning efficacy, there was no significant difference between glycine and sodium bicarbonate, but different mixtures of calcium phosphate, hydroxyapatite, and erythritol were superior to glycine. AA was superior or equal to all other decontamination methods in cleaning. Regarding biocompatibility, AA was more successful in preserving biocompatibility for noncontaminated surfaces compared with contaminated surfaces and when used with erythritol and osteoinductive powders. CONCLUSIONS: AA can efficiently remove contamination without serious damage to the surface. The main drawback of the AA method seems to be its limitation in restoring the biocompatibility of the surface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".