Usage of Bone Replacement Grafts in Periodontics and Oral Implantology and Their Current Levels of Clinical Evidence — A Systematic Assessment
Bibliographic record
Abstract
BACKGROUND: The aim of this review is to evaluate the levels of clinical evidence for bone replacement grafts available in the United States for periodontics and oral implantology purposes. METHODS: A search was performed using PubMed, the Cochrane Library, and Google Scholar for articles relating to the use of bone replacement grafts in implant and/or periodontics by two independent reviewers. Articles unrelated to the topic, not involving patients, not including abstracts, or in languages other than English were excluded. Selected articles were graded according to "levels of evidence" based on guidelines originally introduced by Wright et al. (2003). RESULTS: There was limited published peer-reviewed clinical literature available regarding US commercially available bone replacement grafts in periodontics and oral implantology. Of 144 bone replacement grafts available in the United States according to Avila-Ortiz et al. (2013), only 52 met the inclusion criteria. The majority of materials used were allografts (26 of 93 available in the United States), followed by alloplasts (15 of 30) and xenografts (11 of 21). CONCLUSION: Dental providers should be aware of the limited evidence that qualified for a strong rating supporting the clinical efficacy of these materials for periodontics and oral implantology purposes using the inclusion criteria selected in this study.
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".