Efficacy of air polishing for the non‐surgical treatment of peri‐implant diseases: a systematic review
Bibliographic record
Abstract
FOCUSED QUESTION: In patients suffering from peri-implant diseases, what is the efficacy of air polishing on changing signs of inflammation compared with control treatments (i.e. alternative measures for plaque removal with or without adjunctive antiseptic and/ or antibiotic therapy)? MATERIAL & METHODS: After electronic database and hand search, 10 full-text articles were independently screened by two reviewers. Finally, a total of five studies (six publications) fulfilled the inclusion criteria. The weighted mean difference (WMD) [p; 95% CI] in bleeding on probing- (BOP) (primary outcome) and probing pocket depth- (PD) reductions was estimated using a random effect model. RESULTS: All studies reported on residual BOP scores after therapy. A narrative data synthesis did not reveal any major improvement of bleeding index/ BOP or disease resolution following air polishing over mechanical debridement at mucositis sites. At peri-implantitis sites, WMD in BOP reduction between test and control (mechanical debridement with or without local antiseptic therapy, Er:YAG laser) groups was -23.83% [p = 0.048; 95% CI (-47.47, -0.20)] favouring air polishing over control measures. CONCLUSIONS: While glycine powder air polishing is as effective as the control treatments at mucositis sites, it may improve the efficacy of non-surgical treatment of peri-implantitis over the control measures investigated. A complete disease resolution was commonly not obtained.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".