Radiographic assessment of deproteinized bovine bone mineral (DBBM) and collagen‐stabilized DBBM for transalveolar sinus floor elevation: A 2‐year retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Theoretically, collagen-stabilized deproteinized bovine bone mineral (DBBM-C) has better operability compared with DBBM. DBBM-C avoids dispersing during the transalveolar sinus floor elevation (TSFE) because of its block shape. PURPOSE: To evaluate radiological changes of using DBBM-C in TSFE. MATERIALS AND METHODS: Patients who received TSFE using DBBM (Bio-Oss®) or DBBM-C (Bio-Oss® collagen) with simultaneous implantation were recruited. Graft bone height apically (aGH), endo-sinus bone gain (ESBG), and crest bone level (CBL) were assessed through panoramic radiograph and cone beam computed tomography (CBCT). RESULTS: A total of 138 patients (138 implants) were retrospectively enrolled. After 2 years of implantation, the incidence of postoperative complications was 4.2% (95% CI: 0.9%-11.7%) and 4.5% (95% CI: 0.9%-12.7%) for DBBM and DBBM-C groups, respectively. Measured in panoramic radiograph, ΔaGH of DBBM-C (1.8 mm, SD: 1.4, 95% CI: 1.2-2.4, P = 0.044) group was significantly higher than that of DBBM (1.2 mm, SD: 1.4, 95% CI: 0.7-1.7) after 24 months. No significant differences for ΔCBL were noted during the entire observation period. Measured through CBCT, ESBG was 5.0 (SD: 1.8, 95% CI: 4.1-5.8) mm in DBBM group and 4.6 (SD: 1.6, 95% CI: 3.9-5.3) mm in DBBM-C group 24 months after implantation. The aGH value of DBBM-C group was significantly higher compared with DBBM in CBCT (OR = 1.4, 95% CI: 1.1-1.9, P = 0.020). CONCLUSIONS: DBBM-C could achieve similar bone generation as DBBM in TSFE. Both materials could maintain aGH, ESBG, and CBL relatively stable 2 years after implantation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".