The Use of Carbon-Fiber-Reinforced (CFR) PEEK Material in Orthopedic Implants: A Systematic Review
Bibliographic record
Abstract
Carbon-fiber-reinforced polyetheretherketone (CFR-PEEK) has been successfully used in orthopedic implants. The aim of this systematic review is to investigate the properties, technical data, and safety of CFR-PEEK biomaterial and to evaluate its potential for new innovation in the design of articulating medical devices. A comprehensive search in PubMed and EMBASE was conducted to identify articles relevant to the outcomes of CFR-PEEK orthopedic implants. The search was also expanded by reviewing the reference sections of selected papers and references and benchmark reports provided by content experts. A total of 23 articles were included in this review. There is limited literature available assessing the performance of CFR-PEEK, specifically as an implant material for arthroplasty systems. Nevertheless, available studies strongly support CFR-PEEK as a promising and suitable material for orthopedic implants because of its biocompatibility, material characteristics, and mechanical durability. Future studies should continue to investigate CFR-PEEK's potential benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| 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) | 0.004 | 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".