A doxycycline‐treated hydroxyapatite implant surface attenuates the progression of peri‐implantitis: A radiographic and histological study in mice
Bibliographic record
Abstract
BACKGROUND: Oral rehabilitation with dental implants has become increasingly common; however, the increase of peri-implantitis is a great concern. Doxycycline (DOX) is a widely used antibiotic that inhibits bacteria growth, inflammation, and bone resorption. OBJECTIVES: To evaluate the progression of peri-implantitis of hydroxyapatite (HA)-coated implants with (5 mg/mL, DOX group) or without (HA group) DOX treatment on the surface. MATERIALS AND METHODS: The maxillary first molars of 20 male mice were extracted. Eight weeks later, small titanium screw implants coated with thin HA and treated with or without DOX were placed at the extracted sites. Four weeks after implant placement, half of the animals in both groups were sacrificed, and ligatures were placed around the implant necks in the other half. These mice were sacrificed 4 weeks later. The bone around the implants was examined radiologically and histologically. RESULTS: Four weeks after the ligature placement, the radiographic measurements revealed that peri-implant bone levels of palatal and mesial sites, and histological measurements showed that bone levels of mesial and distal sites in the DOX group were significantly higher than those in the HA group. CONCLUSIONS: The present results indicating that the DOX-treated HA implant surface attenuates the progression of peri-implantitis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".