PEEK materials as an alternative to titanium in dental implants: A systematic review
Bibliographic record
Abstract
PURPOSE: Evaluation of the available research on PEEK materials to find that whether PEEK material has favorable properties and can enhance osseointegration, so that they can be utilize as implants material. MATERIALS AND METHODS: An electronic and structured systematic search was undertaken in May 2018, without any restrictions of time in the Medline/Pubmed, Sci-hub, Ebscohost, Cochrane, and Web of Science databases. To identify other related references further hand search was done. Articles related to PEEK and their applications in implants were only included. Articles not available in abstract form and article other than English language were excluded. RESULTS: Initially, the search resulted in 153 papers. Independent screenings of the abstracts were done by the reviewers to identify the articles related to the question in focus. Sixty-two studies were selected out of which 10 were further excluded due to not in English language. Two additional papers were obtained after hand searching, and finally 54 articles were included in the review. CONCLUSIONS: Surface modification of PEEK seems to enhance the cell adhesion, proliferation, biocompability, and osteogenic properties of PEEK implant materials. PEEK had also influence the biofilm structure and reduces the chances of periimplant inflammations. Further research and more number of controlled clinical trials on PEEK implant is required in near future so that it can replace titanium in future.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".