Wear performance of all-polymer PEEK articulations for a cervical total level arthroplasty system
Bibliographic record
Abstract
Pin-on-plate wear testing of polyetheretherketone (PEEK) was performed in a PEEK-on-PEEK configuration to explore the tribology of this rather uncommon contact configuration. To illustrate its possible application, a total level cervical arthroplasty system was described. Also, it was noted that previous pin-on-plate and simulator studies of this type of material pairing had been conducted. The present study continued the protocol of testing under an adverse loading regime that was originally introduced by Austin et al. to investigate the sensitivity of all-polymer PEEK articulations to previous wear damage. A gravimetric wear assessment protocol was used that avoided vacuum drying. The lower wear of polyacrylonitrile carbon fibre-reinforced PEEK compared with unfilled PEEK, as found by many other studies, was confirmed. PEEK was shown to have remarkably low wear despite previous adverse load testing. The coefficient of friction was found to be quite high (up to 0.5) and this might have clinical implications. Finally, it was noted that the comparison of wear factors and wear amounts from the present study (and other previous studies) with those of the very similar study by Scholes and Unsworth suggested that their laboratory model might be overstating the wear of all-polymer PEEK articulations for potential applications in orthopaedic tribology.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".