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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".