TIMING OF IMPLANT PLACEMENT IN THE ESTHETIC ZONE: A SYSTEMATIC REVIEW AND META-ANALYSIS COMPARING IMMEDIATE AND DELAYED APPROACHES
Bibliographic record
Abstract
Background and Objective: Restoring anterior teeth with implants in the esthetic zone requires a balance between functional integration and esthetic excellence. This systematic review and meta-analysis compared immediate and delayed implant placement protocols with respect to implant survival, marginal bone loss (MBL), and Pink Esthetic Score (PES). Materials and Methods: A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, and Google Scholar up to June 2025. Eligible studies compared immediate and delayed implant placements in the esthetic zone and reported outcomes for survival, MBL, or PES. Data from seven studies were included and analysed using a random-effects model. Risk of bias was assessed using Cochrane and Newcastle-Ottawa tools. Results: Seven studies involving 358 implants (179 immediate, 179 delayed) met the inclusion criteria. Implant survival showed no significant difference (Risk Difference [RD] = 0.00; 95% CI: -0.04 to 0.04; p = 0.99). MBL analysis showed high heterogeneity and no statistical significance (Mean Difference [MD] = -0.05 mm; p = 0.65; I² = 97%). Immediate implant placement was associated with significantly higher esthetic outcomes based on PES (MD = +0.65; 95% CI: 0.06 to 1.24; p = 0.03). Conclusion: Immediate and delayed implant placements have comparable survival rates. However, immediate placement may provide superior esthetic outcomes when appropriate patient selection and surgical technique are ensured. High variability in MBL outcomes underscores the need for standardized protocols in future research.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".