Relative Contributions of Chemistry and Topography to the Osseointegration of Hydroxyapatite Coatings
Bibliographic record
Abstract
The purpose of the current study was to ascertain the relative contributions of surface chemistry and topography to the osseointegration of hydroxyapatite-coated implants. A canine femoral intramedullary implant model was used to compare the osseous response to commercially pure titanium implants that were either polished, grit-blasted, plasma-sprayed with hydroxyapatite, or plasma-sprayed with hydroxyapatite and masked with a very thin layer of titanium using physical vapor deposition (titanium mask). The titanium mask isolated the chemistry of the underlying hydroxyapatite layer without functionally changing its surface topography and morphologic features. At 12 weeks, the bone-implant specimens were prepared for undecalcified thin section histologic evaluation and serial transverse sections were quantified with backscattered scanning electron microscopy for the percentage of bone apposition to the implant surface. Bone apposition averaged 3% for the polished implants and 23% for the grit-blasted implants. Bone apposition to the hydroxyapatite-coated implants averaged 74% whereas bone apposition to the titanium mask implants averaged 59%. Although there was significantly greater osseointegration with the hydroxyapatite-coated implants, 80% of the maximum bone forming response to the implant surfaces developed with the titanium mask implants. This simple, controlled experiment revealed that topography is the dominant factor governing bone apposition to hydroxyapatite-coated 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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".