Soft and Hard Tissue Grafting in Immediate Implant Therapy: A Narrative Review
Bibliographic record
Abstract
Background and Objectives: Immediate implant placement in the esthetic zone presents challenges in maintaining peri-implant tissues due to post-extraction remodeling. Bone grafting has been proposed to support tissue preservation and improve esthetic outcomes. This article reviews the role of grafting in clinical studies and case reports. Materials and Methods: A literature search on PubMed and Google Scholar identified studies focusing on immediate implant placement with grafting. The search strategy included articles from 2012 to 2025, in English, from peer-reviewed journals. Results: Implant survival is possible without grafting in ideal cases, but grafting is often essential in patients with thin biotypes or esthetic demands. Technique and material selection are critical. The socket shield technique shows promise in preserving buccal tissues despite its complexity. Case reports demonstrated stable soft tissues and favorable esthetic outcomes. Conclusions: Grafting should be tailored to the clinical situation. While not always necessary, it is often crucial in compromised sites to ensure long-term esthetic success. Current literature supports predictable outcomes with appropriate grafting protocols.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".