Local hormones and growth factors to enhance orthodontic tooth movement: A systematic review of animal studies
Bibliographic record
Abstract
Abstract Background Prolonged treatment times are the main reason capable of affecting patient compliance with orthodontic treatment. Objectives A systematic review was made to determine whether the local administration of hormones and growth factors effectively enhances orthodontic tooth movement, and to identify possible adverse effects in animal studies. Materials and methods Five databases (PubMed, Scopus, EMBASE, Web of Science and Cochrane Library) were checked for experimental studies reporting one of the following outcomes: orthodontic tooth movement, histological outcomes and side effects. After data extraction, orthodontic tooth movement linked to adjunctive substances was analysed using forest plots. The risk of bias and the quality of evidence were determined with the SYRCLE tool and the GRADE tool, respectively. Results Twenty‐four studies were included in the systematic review. On a specific dose level, epidermal growth factor + liposomes, fibroblast growth factor and prostaglandin E2 + Ca were supported by a moderate level of evidence and rated as highly effective in increasing tooth movement in animal models. Likewise, there was histological evidence of increased bone remodelling after the administration of these substances. Other compounds were supported by a low to very low level of evidence. Conclusions Hormones and growth factors may have a relevant impact upon orthodontic tooth movement rate. In specific formulations, prostaglandin E2, fibroblast growth factor and epidermal growth factor showed promising results.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".