Systemic Antibiotic Prophylaxis Adjunctive to Surgical Reconstructive Peri‐Implantitis Treatment: A Retrospective Study
Bibliographic record
Abstract
AIMS: To evaluate the clinical efficacy of oral systemic antibiotic prophylaxis administered along with the surgical reconstructive peri-implantitis treatment. METHODS: A total of 49 patients exhibiting 70 implants diagnosed with peri-implantitis underwent a surgical reconstructive peri-implantitis treatment. Of them, 27 patients (38 implants) received a single preoperative shot of antibiotics (2 g amoxicillin; Pre-op), 12 patients (19 implants) were prescribed with postoperative antibiotics for 3 days (500 mg amoxicillin, 3 x day, Post-op), and the remaining 10 patients (13 implants) did not receive any systemic antibiotics (No-Ab). Mean probing depth values (mean PDs; primary outcome), bleeding on probing (BOP), plaque (PI), suppuration (Sup), and deepest PDs values (max PD) were assessed prior to surgery (baseline), after 6 and 12 months. To assess the differences in changes in the clinical parameters, and disease resolution (PD ≤ 5 mm, ≤ 1 BOP site and no Sup) among the groups, logistic regression analyses were performed. RESULTS: After 12 months, the mean PD reduction amounted to -1.74 ± 1.56 mm, -1.91 ± 1.88 mm, and -1.13 ± 1.05 mm in the No-Ab, Pre-op, and Post-op groups, respectively, with no significant difference detected among the groups. The BOP was reduced in 60%, 59.3%, and 83.3% of the patients after 12 months in the No-Ab, Pre-op, and Post-op groups, respectively, with no significant differences among them. The PI, Sup and max PD reductions were comparable among the groups. Disease resolution after 12 months was established in 61.5%, 73.7%, and 89.5% of patients in the No-Ab, Pre-op, and Post-op groups (No-Ab vs. Pre-op: p = 0.10, No-Ab vs. Post-op: p = 0.40, Pre-op vs. Post-op: p = 0.84). CONCLUSION: Systemic antibiotic prophylaxis did not improve the clinical outcomes of surgical reconstructive peri-implantitis treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".