Resolution of bone defects of varying dimension and configuration in the marginal portion of the peri‐implant bone
Bibliographic record
Abstract
BACKGROUND: It was demonstrated that a marginal defect of about 1 mm between the bone wall and the metal surface after implant installation can heal with a high degree of bone fill and osseointegration. OBJECTIVE: The aim of the present animal experiment was to study bone healing at implant sites with hard tissue defects of varying dimensions and configuration. MATERIAL AND METHODS: Four Labrador dogs were used. All mandibular premolars and first molars were extracted. After 3 months of healing, five experimental sites, two control (C1, C2) and three test (T1, T2, T3) sites, were identified. In all five sites, custom-made implants with a sand-blasted, large-grit, acid-etched (SLA) surface and with an outer dimension of 3.3x10 mm, were used. In site C1, traditional implant installation was performed. In site C2, the marginal 5 mm of the canal, prepared for the implant, was widened to 5.3 mm using a step-drill. Thus, following the installation of the implant, a circumferential gap occurred between the bone tissue and the metal rod that was 5 mm deep and between 1 and 1.25 mm wide. In test site T1, the canal was widened to establish a marginal gap of 2-2.25 mm. In test sites T2 and T3, the marginal 5 mm of the canal was first widened to 5.3 mm (T2) or 7.3 mm (T3). The buccal bone wall opposite the defect was subsequently removed. Following the placement of a cover screw in sites C2, T1, T2, and T3, a resorbable membrane was placed over the defect. All implants were submerged. After 4 months of healing, block biopsies of each implant site were dissected and processed for ground sectioning. RESULTS: The observations disclosed that four-wall defects of different dimensions (1-2.25 mm wide) that occurred in the marginal portion of the recipient sites following implant installation were resolved during healing. Further, at sites where the buccal bone wall during defect preparation was intentionally removed, healing resulted in defect resolution at the mesial, distal, and lingual aspects. At the buccal aspects, healing was incomplete but the dimension of the defect was reduced by the limited amounts of new bone formation extending from the lateral and apical borders of the defect. CONCLUSION: Wide marginal defects may during healing be filled with bone. In such defects a high degree of osseointegration may occur to implants designed with an SLA surface.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".