Influence of buccal bony crest width on marginal dimensions of peri‐implant hard and soft tissues after implant installation. An experimental study in dogs
Bibliographic record
Abstract
AIM: To evaluate the influence of the width of the buccal bony wall on hard and soft tissue dimensions following implant installation. MATERIAL AND METHODS: Mandibular premolars and first molars of six Labrador dogs were extracted bilaterally. After 3 months of healing, two recipient sites, one on each side of the mandible, were prepared in such a way as to obtain a buccal bony ridge width of about 2 mm in the right (control) and 1 mm in the left sides (test), respectively. Implants were installed with the coronal margin flush with the buccal alveolar bony crest. Abutments were placed and the flaps were sutured to allow a non-submerged healing. After 3 months, the animals were euthanized and ground sections obtained. RESULTS: All implants were completely osseointegrated. In respect to the coronal rough margin of the implant, the most coronal bone-to-implant contact was apically located 1.04 ± 0.91 and 0.94 ± 0.87 mm at the test and control sites, respectively, whereas the top of the bony crest was located 0.30 ± 0.40 mm at the test and 0.57 ± 0.49 mm at the control sites. No statistically significant differences were found. A larger horizontal bone resorption, however, evaluated 1 mm apically to the rough margin, was found at the control (1.1 ± 0.7 mm) compared to the test (0.3 ± 0.3 mm) sites, the difference being statistically significant. A thin peri-implant mucosa (2.4-2.6 mm) was found at implant installation while, after 3 months of healing, a biological width of 3.90-4.40 mm was observed with no statistically significant differences between control and test sites. CONCLUSIONS: A width of the buccal bony wall of 1or 2 mm at implant sites yielded similar results after 3 months of healing in relation of hard tissue and soft tissues dimensions after implant installation.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".