The edentulous ridge expansion (<scp>ERE</scp>) technique an experimental study in the dog
Bibliographic record
Abstract
OBJECTIVE: To compare the healing and bony crest resorption at implants installed conventionally or applying an edentulous ridge expansion (ERE) technique in the maxilla. MATERIAL AND METHODS: In six Labrador dogs, the first and second maxillary incisors were extracted bilaterally. In the left side of the maxilla (Test), the flaps were elevated and the buccal plate of the alveoli and septa was removed. After 3 months of healing, partial-thickness (split) flaps were dissected and the residual alveolar bone was exposed. In the right side of the maxilla, an implant was installed conventionally (Type IV; Control) while, in the left side, the ERE technique was adopted. Hence, an expansion of the buccal bony crest was obtained, and the implant was subsequently installed (Test). After 3 months of healing, biopsies were obtained and ground sections were prepared for histological analyses. RESULTS: A buccal vertical resorption of the bony crest of 2.2 ± 1.2 mm and 1.6 ± 0.7 mm was found at the test and control sites, respectively. The difference, however, did not reach statistical significance. The coronal level of osseointegration at the buccal aspect was located at 3.1 ± 1.0 mm and 2.2 ± 0.7 mm from the implant shoulder at the test and control sites, respectively, the difference being statistically significant. The mean values of the mineralized bone-to-implant contact (MBIC%) ranged from 43% to 48% at the buccal and lingual sites. No differences reached statistical significance. CONCLUSIONS: Implants installed by applying an ERE technique may osseointegrate similarly to conventional implant installation. However, vertical and horizontal resorption of the displaced buccal bony wall occurred as well.
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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 | 0.001 |
| 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".