A new multiple anti-infective non-surgical therapy in the treatment of peri-implantitis: a case series
Bibliographic record
Abstract
BACKGROUND: Peri-implantitis is a frequent disease that may lead to implant loss. The aim of this case series was to evaluate the clinical results of a new non-surgical treatment protocol. METHODS: Fifteen patients with dental implants affected by peri-implantitis were treated with a multiple anti-infective non-surgical treatment (MAINST) which included two steps: 1) supra-gingival decontamination of the lesion and sub-gingival treatment with a controlled-release topical doxycycline; 2) after one week, a session of supra and sub gingival air polishing with Erythritol powder and ultrasonic debridement (where calculus was present) of the whole oral cavity was performed along with a second application of topical doxycycline around the infected implant. Primary outcome measures were: implant failure; complications and adverse events; recurrence of peri-implantitis; secondary outcome measure were presence of Plaque (PI), Bleeding on Probing (BOP), Probing Pocket Depth (PPD). Recession (REC), Relative Attachment level (RAL). RESULTS: Neither implant failure nor complications nor adverse events were reported. Statistically (P<0.01) and clinically significant reductions between baseline and 1 year of PI (100% vs. 13.9%, 95% CI: 72.4% to 93.7%); BOP (98.5% vs. 4.5%, 95% CI: 85.4% to 98.5%) and PPD (7.89 vs. 3.16 mm, 95% CI: -5.67 to -3.77), were detected. At baseline, all 15 patients had a PPD>5 mm at the affected implant(s), whereas only 3.7% at 3-month follow-up a PPD>5 mm, and none at 6 and 12 months. CONCLUSIONS: Within the limits of this study, the MAINST protocol showed improvement of clinical parameters for the treatment of peri-implantitis, which were maintained for up to 12 months.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".