<i>In vivo</i> biocompatibility testing of peek polymer for a spinal implant system: A study in rabbits
Bibliographic record
Abstract
We are developing a new spinal implant system (SIS) without fusion (bone graft). This SIS is made from two materials, metal and polyetheretherketone (PEEK) polymer. The Food and Drug Administration recommended testing in vivo, in an animal model, whether the PEEK polymer could be used in a SIS without any harm of wear debris to the nervous tissue (spinal cord and nerve roots). The objective was to evaluate the biological response of the spinal cord and nerve roots (dura mater) to PEEK polymer particles. Twenty-four female New Zealand white rabbits were used. The rabbits were divided into three groups: test (n = 12), control (n = 9), and sham (n = 3). During the surgery, the test group received the PEEK particle injections (5 x 10(7) particles per site, lumbar and thoracic), while the control group received only the vehicle (0.9% saline solution). The sham group had the same surgical approach without injection. In each group, the rabbits were euthanized at 1, 4, and 12 weeks postsurgery. The macroscopic and semiquantitative histologic analyses of the spinal cords (dura mater) showed normal vascularization and particle adherence to the connective tissue especially at the injection sites. Neither necrosis nor swelling of the dura mater and nerve roots was observed. The PEEK polymer is harmless to the spinal cord; thus it might be used as component in the spinal implant system.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".