Effects of metformin on the bioactivity and osseointegration of dental implants: A systematic review
Bibliographic record
Abstract
Dental implants are prosthetic devices that are surgically placed in direct contact with the jawbone to support intra-oral functions and esthetics. Diabetes mellitus may contribute to peri-implant bone loss. During the last few years, there have been attempts to reduce this bone loss and improve the survival rate of implants. Metformin, an anti-diabetic drug known for its osteogenic properties, is thought to prevent peri-implant bone loss in diabetic patients. Although several studies have been conducted to study metformin's effect on diabetic and non-diabetic study models, no systematic review has analyzed and summarized these studies critically. Therefore, the objectives of this systematic review were to summarize the outcomes of these studies and critically appraise them. Seven studies were included in this systematic review. Four studies used only animal models, two used both animal and cell culture models, and one used only cell culture studies. The general characteristics and outcomes of the included studies were summarized, and Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines were used to assess the quality of the animal studies. In vitro studies indicate that metformin may induce stem cells to undergo osteoblastic differentiation to produce a higher amount of bone and may also improve osseointegration. Nevertheless, several studies had potential sources of bias. Therefore, it is recommended that emphasis be placed on increasing the quality of future animal studies and human trials to determine the effects of metformin on the osseointegration of dental implants. Future studies are needed with adequate follow-up to evaluate the efficacy of metformin in improving the osseointegration of dental implants.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".