Peri‐implant tissues morphometry at <scp>SLA</scp>ctive surfaces. An experimental study in the dog
Bibliographic record
Abstract
OBJECTIVE: The objective was to study tissue components around implants with highly hydrophilic surfaces during early healing. MATERIALS AND METHODS: In 12 Labrador dogs, the second and third mandibular premolars were extracted bilaterally. After 3 months of healing, full-thickness flaps were elevated in the edentulous region of one side of the mandible. An implant was installed, and the flaps were sutured to allow a non-submerged healing. The timing of the implant installations in the other side of the mandible until sacrifices were performed in such a way to collect biopsies representing healing after 4, 7, 15, and 60 days. An n = 6 was achieved for each healing period. Paraffin sections were obtained for morphometric analyses. RESULTS: Provisional matrix with a percentage of 32.9 ± 16.7% was found already after 4 days. This percentage became 37.3 ± 8.5%, 24.3 ± 9.1%, and 1.6 ± 1.7 after 7, 15, and 60 days, respectively. New bone was found after 7 days of healing, at a percentage of 26.2 ± 3.2%. This proportion increased to 36.0 ± 9.6% and 50.4 ± 8.3% after 15 and 60 days, respectively. Marrow spaces free from a blood clot, inflammatory cells, and provisional matrix represented a low proportion of the tissues after 4 days (1.6 ± 2.4%). This proportion increased over time to 9.2 ± 6.4%, 20.3 ± 12.9%, and 37.9 ± 9.6%, respectively. The percentage of old bone was noted in a similar percentage (~8%) up to 15 days. The percentage decreased to ~5% at 60-day of observation. CONCLUSION: The tissue changes observed during the healing were similar to those from historic controls studying healing in a chamber adjacent to implants. Hence, the characteristics of the implant surfaces may not be reflected in the tissue composition adjacent to the implant but rather affect the adhesion of tissue onto the implant surfaces.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".