The Impact of Autologous Platelet Concentrates on the Periapical Tissues and Root Development of Replanted Teeth: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: In many cases, the replanted teeth may undergo resorption or ankyloses. Recent studies show that autologous platelet concentrates (APCs) may improve the outcomes of tooth replantation. The aim of this systematic review was to summarize and critically appraise the currently available literature on the use of APCs before tooth replantation. METHODOLOGY: An electronic search was conducted on the following research databases: PubMed/MEDLINE, ISI Web of Science, EMBASE and Scopus. The following medical subject heading (MeSH) keywords used were: ((tooth replantation) OR (replanted tooth) OR (teeth replantation) OR (replanted teeth)) AND ((autologous platelet concentrate) OR (platelet-rich plasma) OR (platelet-rich fibrin) OR (autologous platelet)). The studies' data was extracted, and the research' quality was rated using the CARE and ARRIVE protocols. RESULTS: Ten case reports and three animal studies, one cell study and one study, which included both animal and in vitro experiments, were included in this review. In majority of the studies, APCs improved the outcomes of tooth replantation. However, there were various sources of bias in the most of the research, which may have influenced the results. CONCLUSIONS: Although majority of the studies indicate that APCs may improve outcomes of tooth replantation, majority of the studies contained numerous sources of bias. Additionally, the sample size of the included subjects is inadequate to predict the clinical efficacy of APCs in management of replanted teeth. Large-scale, multi-center and long-term studies are required to ascertain the efficacy of APCs in improve the outcomes of tooth replantation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".