Osseointegration on implant surfaces previously contaminated with plaque
Bibliographic record
Abstract
This study investigated whether osseointegration can occur on a surface which had previously been coated with dental plaque. The mandibular premolar regions of four young adult Labrador dogs were used for the study. The lower premolars (P1, P2, P3, and P4) were extracted on either side of the mandibles. Following a 12-week healing period, three 3.75 mm x 13 mm commercially pure titanium implants (Nobel BiocareAB, Gothenburg, Sweden) were partially inserted in one side of each mandible. This resulted in some threads protruding from the tissues into the oral cavity. Plaque was allowed to accumulate on the exposed implant surfaces. Following a 5-week healing period, the contaminated parts of each implant were treated using three different cleaning techniques: (1) swabbing with supersaturated citric acid for 30 s on a cotton pellet followed by rinsing with physiological saline, (2) cleansing with a toothbrush and physiological saline only for 1 min, and (3) swabbing with 10% hydrogen peroxide (H2O2) on a cotton pellet for 1 min followed by rinsing with physiological saline. The treated implants and one previously unused implant (control) were then placed into freshly prepared tapped sites to the full implant length on the contralateral sides of the mandibles. Following 11 weeks of healing, biopsies were obtained and ground sections prepared for histomorphometric analysis. All treatment modalities were associated with direct bone to implant contact on the portion of implant surface previously exposed to the oral environment. In conclusion, The results demonstrate that osseointegration can occur to surfaces that were plaque contaminated and cleaned by different methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".