Remodeling of calvarial graft in increased atrophic maxillary thickness. A prospective clinical study
Bibliographic record
Abstract
PURPOSE: This study evaluated the autogenous graft resorption rate in a calvarial block graft in the anterior region of an atrophic maxilla and compared it with the thickness of the remaining ridge. MATERIALS AND METHODS: Twelve patients were included in the study. They were submitted to cranial calotte graft surgery, and there were 40 blocks in total. The thicknesses of the ridges in the crest, middle and apical regions of the blocks were evaluated by computed tomography scan at the times: preoperative (T0), 48 hours (T1) and 6 months (T2) after the reconstructions. RESULTS: The resorption of the blocks from T1 to T2 was 13.4%. The greatest remodeling occurred in the alveolar bone crest (20.07%), followed by the middle portion (12.28%), and the apical region (9.5%), but the three regions did not significantly differ between times T1 and T2 (crest P = .07, middle P = .124, apical P = .131). Recipient site with the lowest thickness had the greatest resorption rates (up to 2 mm = 17.6%; from 2 to 4 mm = 17.52%) while than those with a thickness greater than 4 mm had a mean resorption of 8.81%. CONCLUSIONS: The resorption of the grafts in this study was 13.4%. Higher resorption rates were observed in the alveolar crest areas, where the ridges were less thick.
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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 | 0.001 |
| 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".