Bone Graft Healing in Reconstruction of Maxillary Atrophy
Bibliographic record
Abstract
PURPOSE: Evaluate correlations between volume change for iliac crest bone grafts in maxillary reconstruction (graft volume change [GVC]) and bone mineral density (BMD), bone volume fraction (BVF), hematologic bone metabolic factors (I), and identify indicators of implant failure (II). MATERIAL AND METHODS: Forty-six consecutive patients had their edentulous atrophic maxilla reconstructed with free autogenous bone grafts from anterior iliac crest. Endosteal implants were placed 6 months after graft healing. Computer tomography was performed after 3 weeks and 6 months after grafting. Bone biopsies were taken from the internal table of donor site for calculation (BVF), and blood samples were collected. Implant stability was measured at placement with resonance frequency analysis and expressed as implant stability quotient (ISQ). Implant failure was registered. RESULTS: GVC in onlay bone graft was 37%. The BVF in iliac crest biopsies was 32%. Serum-IGFBP3 differed with 79% of the samples over normal range. Fifteen patients had one or more implant failures prior to loading (early failures). Forty-two patients were followed for a minimum of 3 years after implant loading and, in addition, 6/42 patients had one or more implants removed during the follow-up (late failures). GVC correlated to decreased BMD of lumbar vertebrae L2-L4 (Kruskal-Wallis test, p=.017). No correlation was found between GVC and hematologic factors (Pearson correlation test) or between GVC and BVF (Kruskal-Wallis test). No correlation was found between ISQ and GVC (Pearson correlation test, p=.865). The association between implant failures and the described factors were evaluated, and no significant correlations were found (unconditional logistic regression). CONCLUSION: Onlay bone grafts decrease 37% during initial healing period, which correlate to BMD of lumbar vertebrae L2-L4. No other evaluated parameters could explain GVC. The evaluated factors could not explain implant failure.
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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) | 0.002 | 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".