HEALING OF BUCCAL DEHISCENCE DEFECTS AT IMPLANTS INSTALLED IMMEDIATELY INTO \nEXTRACTION SOCKETS – AN EXPERIMENTALSTUDY IN DOGS
Bibliographic record
Abstract
Abstract \n \nAIM: \n \nTo evaluate the influence of implant positioning into extraction sockets on bone formation at buccal alveolar dehiscence defects. \n \nMATERIAL AND METHODS: \n \nIn six Labrador dogs the pulp tissue of the mesial roots of (4) P(4) was removed and the root canals were filled. Flaps were elevated bilaterally, the premolars hemi-sectioned and the distal roots removed. The implants were placed in contact with either the buccal (test site) or with the lingual (control site) bony wall of the extraction sockets. Healing abutments were affixed and triangular buccal bony dehiscence defects, about 2.7 mm deep and 3.5 mm wide, were then prepared. No regenerative procedures were done and a non-submerged healing was allowed. After 4 months of healing, block sections of the implant sites were obtained for histological processing and peri-implant tissue assessment. \n \nRESULTS: \n \nAfter 4 months of healing, the bony crest and the coronal border of osseointegration at the test sites were located 1.71 ± 1.20 and 2.50 ± 1.21 mm apically to the implant shoulder, respectively. At the control sites, the corresponding values were 0.68 ± 0.63 and 1.69 ± 0.99 mm, respectively. The differences between test and control reached statistical significance (P < 0.05). Residual marginal bone defects were found both at the test and control sites. A statistically significant difference between test and control sites was only found at the lingual aspects (depth 2.09 ± 1.01 and 1.01 ± 0.48 mm, respectively). Similar heights of the buccal biological width were observed at both sites (about 5.1 mm). \n \nCONCLUSIONS: \n \nThe placement of implants in a lingual position of the extraction sockets allowed a higher degree of bone formation at buccal alveolar dehiscence defects compared with a buccal positioning.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".