Autogenous Transplantation of Teeth Across Clinical Indications: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Autogenous tooth transplantation offers a biologically favorable approach to tooth replacement, preserving the periodontal ligament, promoting alveolar development, and maintaining proprioception. Background/Objectives: Its broader clinical applicability is limited by variability in techniques and outcome definitions. This systematic review and meta-analysis evaluated the clinical success of autogenous tooth transplantation across donor tooth types, developmental stages, surgical techniques, and fixation methods. Methods: Following PRISMA 2020 guidelines and a PROSPERO-registered protocol (CRD42024625550), five databases and the gray literature were searched through July 2025. Eligible studies reported clinical outcomes for autogenous tooth transplantation. Risk of bias was assessed using the Newcastle–Ottawa Scale. A random-effects meta-analysis of logit-transformed proportions was conducted, with subgroup and sensitivity analyses by tooth type, root development stage, surgical technique, and fixation method. Results: Twenty studies involving 1366 transplanted teeth were included. The pooled success rate was 94.0% (95% CI: 22.5–99.9%) across follow-up periods ranging from one month to twenty-nine years. However, interpretation is limited by distinct heterogeneity (I2 = 99.8%) and the wide confidence interval. Subgroup analyses by tooth type, root maturity, surgical technique, and fixation method are, therefore, emphasized to support clinical interpretation. Conclusions: Autogenous tooth transplantation achieves consistently high success across clinical contexts when biologic handling is respected. These findings support its broader use in dental and orthodontic practice and underscore the need for standardized outcome reporting and prospective research. Interpretation is limited by heterogeneity and variation in reporting standards.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".