Full block or split block?—Comparison of two different autogenous block grafting techniques for alveolar ridge reconstruction
Bibliographic record
Abstract
OBJECTIVE: To evaluate radiographic bone gain after alveolar ridge augmentation with two different designs of autogenous block graft harvested from the mandible. MATERIALS AND METHODS: Alveolar ridge defects were evaluated by preoperative cone beam computed tomography (CBCT) and grafted in a staged approach using intraoral block grafts. The ridge augmentation was either performed using the full-block technique (group 1) or the split-block technique (cortical plate with autogenous bone chips) (group 2). After 4 months of bone healing, a further CBCT scan was performed before implant placement. Horizontal and vertical bone gain were measured. RESULTS: In this retrospective study, 91 patients were grafted with block grafts (36 patients with full-block grafts; 55 patients with split-block grafts) resulting in 171 block grafts in total. The mean horizontal bone gain was 3.37 ± 0.71 mm in group 1 and 5.79 ± 2.20 mm in group 2. A linear mixed-effect model also showed a statistically significant group difference (p < 0.001, estimate: 3.455, 95% CI: [2.082-4.829]). The mean vertical bone gain was 2.85 ± 0.73 mm in group 1 and 7.60 ± 1.87 mm in group 2. A linear mixed-effect model also showed a statistically significant group difference (p: 0.029, estimate: 3.126, 95% CI: [0.718-5.557]). Mean marginal bone level was 0.33 ± 0.37 mm (group 1) and 0.17 ± 0.29 mm (group 2). CONCLUSION: The split-block technique resulted in a greater bone gain than the full-block technique. This effect was observed in both the vertical and the horizontal dimensions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".