Tissue response to the components of a hydroxyapatite‐coated composite femoral implant
Bibliographic record
Abstract
Bone loss around femoral implants used for THA is a persistent clinical concern. It may be caused by stress shielding, generally attributed to a mismatch in stiffness between the implants and host bone. In this regard, a fatigue resistant, carbon fiber (CF) composite femoral implant with bone-matching stiffness has been developed. This study evaluated the tissue response to the three material components of this implant in normal and textured (blasted with 24 grit alumina) surfaces: the hydroxyapatite (HA) coating, the CF composite and the intermediate crystalline HA particulate composite layer to bond to the HA coating (blended). Sprague-Dawley rats underwent bilateral femoral implantation each receiving two rod-like implants. Bone apposition to the HA (37%) and textured Ti (41%) implants was not significantly different. Bone apposition to the untextured CF (14%) and blended (19%) implants and polished Ti (8%) implants was significantly lower. Bone apposition to the textured CF (9%) and blended (11%) implants was lower (but not statistically from the as received or untextured counterparts). Nearly all sections from femurs containing CF implants presented CF debris. There was no evidence of localized bone loss or any strong immune response associated with any of the implant materials. All materials were well tolerated with minimal inflammation despite the presence of particulate debris. The high degree of bone apposition to the HA-coated composite implants and the lack of short-term inflammation and adverse tissue response to the three material implant component support continued evaluation of this composite technology for use in THA.
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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) | 0.000 | 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) | 0.001 | 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".