Surface Modification Strategies to Improve the Osseointegration of Poly(etheretherketone) and Its Composites
Bibliographic record
Abstract
In the last 5 years, a wide variety of surface modification strategies are explored to improve the integration of poly(etheretherketone) (PEEK) implants with bone. Since PEEK does not support bone on-growth, its surface properties need to be tailored to promote osteogenesis at the bone-implant interface. Surface modifications applied to achieve this response range from simple surface morphology changes to the deposition of osteoconductive coatings. Of the many methods, titanium and/or hydroxyapatite coatings, extrusion to create surface pores, and an accelerated neutral atom beam treatment have been approved by the U.S. Food and Drug Administration to improve the integration of PEEK spinal cages. The success of these surface modifications brings hope for the clinical translation of other techniques in the future, but there are several limitations that may be preventing other treatments from reaching the clinic. This review describes numerous strategies that have been applied to PEEK-based implants for improving their osseointegration and enhancing their antibacterial properties. The review concludes with a discussion about future directions for the field and provides suggestions for advancing clinical translation of surface-modified PEEK implants to improve the lives of patients in need of these 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".