Relationship between Systemic Bone Mineral Density and Local Bone Quality as Effectors of Dental Implant Survival
Bibliographic record
Abstract
PURPOSE: This study aimed to assess (1) the relationship of systemic bone mineral density (BMD) and osteoporotic status with the surgeon's subjective assessment of local jawbone quality, and (2) whether the surgeon's subjective assessment of local jawbone quality is a predictor of implant failure. MATERIALS AND METHODS: A retrospective analysis of 2,867 dental implants placed in 645 patients was accomplished. The surgeon's assessment of bone quality at the time of dental implant placement was recorded. Of those, 208 patients with 701 implants had BMD data available within 3 years. Statistical analyses were conducted to determine relationships between BMD, osteoporotic status, and local jawbone quality and to determine the relationship between local jawbone quality and implant survival. RESULTS: There was no association between systemic BMD and the surgeon's assessment of bone quality (p =.52) nor between osteoporotic status and the surgeon's assessment of local jawbone quality (Spearman rank correlation coefficient=0.08). Additional retrospective analysis revealed implants placed in moderate- (hazard ratio=1.67; p=.043) or poor-quality (HR=3.45, p< .001) bone (surgeon's assessment) were significantly more likely to fail than implants placed in good-quality bone. CONCLUSION: Systemic BMD and osteoporotic status are not associated with local jawbone quality. Implants placed in good-quality bone, as assessed subjectively by the surgeon at the time of implant placement, have significantly better survival characteristics than implants placed in moderate-/poor-quality bone.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".