<scp>Polyetheretherketone</scp> implant surface functionalization technologies and the need for a transparent quality evaluation system
Bibliographic record
Abstract
Abstract For bone implants, osseointegration resulting in a good and fast bone–implant contact is of primary importance to secure a proper implant function and to avoid implant loosening or inflammation resulting in necessary revision surgeries causing pain to the patients and immense costs. In particular, polyetheretherketone (PEEK) is a promising implant material due to the close mechanical properties to bone, but it is entirely bio‐inert, hindering osseointegration and making surface functionalization necessary. Many different surface functionalization technologies have been reported of both physical and chemical nature. The same is true for the other prominent implant materials titanium and ceramics. Although they already have inherently better osseointegration than PEEK, they are much harder and stiffer than bone and brittle in the case of ceramics. Surface functionalization, which can be subdivided into surface coating and material modification, needs to be judged from a quality and safety viewpoint. However, a literature research resulted in the realization that no quality standard yet exists for implant surface functionalizations. This makes it difficult to near impossible to compare the safety and performance of different surface‐functionalized bone implants, clearly showing the need to establish a transparent quality evaluation system for bone implants. This perspective article gives the state of the art and then develops a quality evaluation system based on six main categories as important benchmarks for the quality of surface‐functionalized bone implant materials. A simple catalog of questions can be answered, and from the resulting scores the Safety and Performance Evidence Level (SPEL) representing the safety and quality of a given implant can be calculated as a percentage. This simple SPEL system allows an easy and transparent judgment and comparison of bone implants, ensuring the easy identification of safe and well‐performing high‐quality bone implants in the future. © 2020 The Authors. Polymer International published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".