Osteogenic Potential of Dental Mesenchymal Stem Cells in Preclinical Studies: A Systematic Review Using Modified ARRIVE and CONSORT Guidelines
Bibliographic record
Abstract
Background and Objective. Dental stem cell-based tissue engineered constructs are emerging as a promising alternative to autologous bone transfer for treating bone defects. The purpose of this review is to systematically assess the preclinical in vivo and in vitro studies which have evaluated the efficacy of dental stem cells on bone regeneration. Methods. A literature search was conducted in Ovid Medline, Embase, PubMed, and Web of Science up to October 2014. Implantation of dental stem cells in animal models for evaluating bone regeneration and/or in vitro studies demonstrating osteogenic potential of dental stem cells were included. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were used to ensure the quality of the search. Modified ARRIVE (Animal research: reporting in invivo experiments) and CONSORT (Consolidated reporting of trials) were used to critically analyze the selected studies. Results. From 1914 citations, 207 full-text articles were screened and 137 studies were included in this review. Because of the heterogeneity observed in the studies selected, meta-analysis was not possible. Conclusion. Both in vivo and in vitro studies indicate the potential use of dental stem cells in bone regeneration. However well-designed randomized animal trials are needed before moving into clinical trials.
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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 | 0.071 | 0.124 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.017 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".