The effect of fibroblast growth factor-2 on the outcomes of tooth replantation: A systematic review of animal studies
Bibliographic record
Abstract
Background/Aim: The ideal treatment of tooth avulsion is replantation. However, replanting teeth may lead to root resorption. Fibroblast growth factor-2 (FGF-2) is a cytokine that plays an important role in wound repair and tissue regeneration. Recently, FGF-2 has been studied a potential regenerative agent to prevent root resorption and ankylosis. The aim of this review is to analyze and summarize the currently available literature focusing on using FGF-2 based regenerative modalities to improve the outcomes of tooth replantation. Materials and Methods: An electronic search was conducted via PubMed/Medline, Google Scholar and ISI Web of Knowledge, using the Medical Subject Headings (MeSH) terms “Basic fibroblast growth factor,” “Fibroblast growth factor-2,” “tooth replantation,” and “replantation” for studies published between January 2001 and June 2021. Data was extracted and quality assessment was carried using the ARRIVE guidelines. Results: Nine animal studies were included in this review. In six studies, FGF-2 had a favorable effect on the tissue regeneration around roots of replanted teeth when compared to other treatment groups. However, quality assessment of the studies revealed many sources of bias and deficiencies in the studies. Conclusions: Within the limitations of this study, it may be concluded that FGF-2 may improve the outcomes of delayed replantation of avulsed teeth. However, more long-term animal studies, with improved experimental designs, and clinical trials are required to determine the clinical potential of the growth factor in improving the outcomes of delayed tooth replantation.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".