Deproteinized Bovine Bone Mineral With Collagen for Anterior Maxillary Ridge Augmentation: A Retrospective Cohort Study
Bibliographic record
Abstract
ABSTRACT Objectives This study aimed to assess the effects of deproteinized bovine bone mineral with collagen (DBBMC) combined with deproteinized bovine bone mineral (DBBM) on facial alveolar bone augmentation in the anterior maxillary region. Materials and Methods Patients receiving dental implant placement with simultaneous lateral bone augmentation using DBBM (control group) or DBBMC combined with DBBM (test group) were included in the study. The radiographic assessment of facial alveolar bone, such as facial horizontal bone thickness (FHBT), facial vertical bone level (FVBL), and square of facial bone (SFB), was taken by cone beam computed tomography (CBCT). Generalized estimated equation (GEE) was performed to identify influencing factors associated with the contraction in square of facial bone (SFBC). Results A total of 164 implants from 164 patients were included in this study. After 6 months post‐surgery, the SFBC and the alterations of FHBT and FVBL in the test group were significantly higher than those in the control group (p < 0.05). After 1–2 years after restorations, the SFBC and the alterations of FHBT and FVBL in the test group were significantly lower than those in the control group (p < 0.05). Spearman correlation analysis demonstrated a positive correlation between the alterations of FVBL and FHBT at the implant platform level in the test group (rs = 0.322, p = 0.001; rs = 0.349, p = 0.002). Implant timing of early loading (p = 0.014) and the implant site of the central incisor (p = 0.040) were significantly associated with the SFBC. Conclusions The applications of DBBMC combined with DBBM achieved better facial alveolar bone augmentation in the anterior maxillary region, especially in early implant placement.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".