Hard and soft tissue changes around implants installed in regular‐sized and reduced alveolar bony ridges. An experimental study in dogs
Bibliographic record
Abstract
OBJECTIVE: To study bony and soft tissue changes at implants installed in alveolar bony ridges of different widths. MATERIAL AND METHODS: In 6 Labrador dogs, the mandibular premolars and first molars were extracted, and a buccal defect was created in the left side at the third and fourth premolars by removing the buccal bone and the inter-radicular and interdental septa. Three months after tooth extraction, full-thickness mucoperiosteal flaps were elevated, and implants were installed, two at the reduced (test) and two at the regular-sized ridges (control). Narrow or wide abutments were affixed to the implants. After 3 months, biopsies were harvested, and ground sections prepared for histological evaluation. RESULTS: A higher vertical buccal bony crest resorption was found at the test (1.5 ± 0.7 mm and 1.0 ± 0.7 mm) compared to the control implants (1.0 ± 0.5 mm and 0.7 ± 0.4 mm), for both wide and narrow abutment sites. A higher horizontal alveolar resorption was identified at the control compared to the test implants. The difference was significant for narrow abutment sites. The peri-implant mucosa was more coronally positioned at the narrow abutment, in the test sites, while for the control sites, the mucosal adaptation was more coronal at the wide abutment sites. These differences, however, did not reach statistical significance. CONCLUSIONS: Implants installed in regular-sized alveolar ridges had a higher horizontal, but a lower vertical buccal bony crest resorption compared to implants installed in reduced alveolar ridges. Narrow abutments in reduced ridges as well as wide abutments in regular-sized ridges yielded less soft tissue recession compared to their counterparts.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".