[Effect evaluation on different treatment methods of the gap of immediate implantation in canine posterior mandible].
Bibliographic record
Abstract
PURPOSE: This study was aimed to explore the formation of new bone after different methods of filling the gap between the extraction socket and the implant in immediate implantation of the canine mandibular molar area. METHODS: Six Labrador dogs aged 1.5-2.0 years were used as the experimental subjects. The fourth premolar and the first molar were extracted from the mandible of each dog, and then 4 dental implants (Astra Tech, 4.0 mm × 10 mm) were implanted respectively. The mesial and distal gaps between dental implants and the walls of extraction socket were treated in three methods: blank group (group NN), gelatin sponge with colloidal silver (Gelatamp) group (group EN), Gelatamp + absorbable collagen membrane (CM) group (group EG). At 12 weeks, the dogs were euthanized, and specimens were collected for micro-CT scanning and histological analysis. SPSS 25.0 software package was used for statistical analysis. RESULTS: The survival rate at 12 weeks after implantation was 100%. Micro-CT scan results showed no significant differences in new bone height, bone mineral density (BMD), bone volume fraction (BV/TV), bone surface area bone volume ratio (BS/TV), bone trabecular thickness (Tb.Th), bone trabecular number (Tb.N) and bone trabecular separation (Tb.Sp) among different groups. Histological analysis showed no significant differences in the area of new bone formation and bone-implant contact (BIC). CONCLUSIONS: After different treatments are performed on the gap between implants and extraction sockets, Gelatamp alone or in combination with CM has no significant effect on new bone formation, BIC, BMD, BV/TV, BS/TV, Tb.Th, Tb.N and Tb.Sp around implant.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".