Platelet-rich fibrin in combination with mandibular or maxillary non-vascularized bone graft: a systematic review
Bibliographic record
Abstract
Introduction: Autogenous non-vascularized bone graft (NVBG) is the gold standard for treating defects smaller than 6 cm.In the maxillofacial region, NVBG is useful to treat periodontal defects, congenital defects, ridge atrophy, sinus augmentation, etc.The addition of autogenous platelet-rich fibrin (PRF) was reported to improve clinical outcomes.However, no high-quality evidence was ever made regarding this treatment combination.Objectives: To assess the evidence of adding PRF to autogenous NVBG in the mandibular and maxillary regions.Material and methods: Literature searches were conducted in PubMed, EMBASE, ProQuest, Scopus, EBSCOhost, and Science Direct to identify randomized controlled trials comparing PRF combined with autogenous NVBG and autogenous NVBG alone.The main outcomes were quantitative bone regeneration measured as height, length, volume, percentage, or other possible quantitative outcomes.Results: Five studies were included in this systematic review comparing PRF and autogenous NVBG to ANVBG alone, with a total of 130 patients with ridge resorption, periodontitis with furcation involvement, or alveolar cleft.Measurements of outcomes were displayed as gained width, vertical bone changes, and volumetric changes.Two studies presented significant differences in the tested group.Conclusions: PRF may improve bone regeneration in combination with autogenous NVBG.Future studies need to investigate with a larger population, size of defects, and better outcome measurements.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".