Immediate implant placement with or without socket grafting: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: To assess the effect of grafting the gap (SG) between the implant surface and alveolar socket on hard and soft tissue changes following single immediate implant placement (IIP). MATERIALS AND METHODS: Two independent reviewers conducted an electronic literature search in Pubmed, Web of Science, Embase and Cochrane databases as well as a manual search to identify eligible clinical studies up to August 2021. Randomized controlled trials (RCTs) comparing IIP with and without SG were included for a qualitative analysis. Meta-analyses were performed when possible. RESULTS: Out of 3627 records, 15 RCTs were selected and reported on 577 patients who received 604 single immediate implants (IIP + SG: 298 implants in 292 patients; IIP: 306 implants in 285 patients) with a mean follow-up ranging from 4 to 36 months. Two RCTs showed low risk of bias. Meta-analysis revealed 0.59 mm (95% CI [0.41; 0.78], p < 0.001) or 54% less horizontal buccal bone resorption following IIP + SG when compared to IIP alone. In addition, 0.58 mm (95% CI [0.28; 0.88], p < 0.001) less apical migration of the midfacial soft tissue level was found when immediate implants were installed with SG. A trend towards less distal papillary recession was found (MD 0.60 mm, 95% CI [-0.08; 1.28], p = 0.080) when SG was performed, while mesial papillae appeared not significantly affected by SG. Vertical buccal bone changes were also not significantly affected by SG. Insufficient data were available for meta-analyses on horizontal midfacial soft tissue changes, pink esthetic score, marginal bone level changes, probing depth and bleeding on probing. Based on GRADE guidelines, a moderate recommendation for SG following IIP can be made. CONCLUSION: SG may contribute to horizontal bone preservation and soft tissue stability at the midfacial aspect of immediate implants. Therefore, SG should be considered as an adjunct to IIP in clinical practice.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.042 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".