Microstructural volumetric analysis of vertical alveolar ridge augmentation using autogenous tooth roots
Bibliographic record
Abstract
BACKGROUND: To volumetrically assess the bone microstructure following vertical alveolar ridge augmentation using differently conditioned autogenous tooth roots (TR) and second-stage implant placement. MATERIALS AND METHODS: The upper premolars were bilaterally extracted in n = 4 beagle dogs and randomly assigned to either autoclavation (TR-A) or no additional treatment (TR-C). Subsequently, TR were used as block grafts for vertical alveolar ridge augmentation in both lower quadrants. At 12 weeks, titanium implants were inserted and left to heal 3 weeks. Microcomputed tomography was used to quantify bone volume per tissue volume (BV/TV), trabecular thickness (Tb.Th), and trabecular spacing (Tb.Sp) at vestibular (v) and oral (o) aspects along the implant and in the augmented upper half of the implant, respectively. RESULTS: Median BV/TV [TR-C: 51.33% (v) and 70.42% (o) vs TR-A: 44.05% (v) and 64.46% (o)], Tb.th [TR-C: 0.22 mm (v) and 0.27 mm (o) vs TR-A: 0.23 mm (v) and 0.29 mm (o)] and Tb.Sp [TR-C: 0.26 mm (v) and 0.13 mm (o) vs TR-A: 0.29 μm (v) and 0.15 mm (o)] values were comparable in both groups. CONCLUSION: Both TR-C and TR-A grafts were associated with a comparable bone microstructure within the grafted area.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".