A “best fit” approach for synergistic surface parameters to guide the design of candidate implant surfaces
Bibliographic record
Abstract
Human bone resorption surfaces can provide a template for endosseous implant surface design. We characterized the topography of such sites using four synergistic parameters (fractal dimension, lacunarity, porosity, and surface roughness) and compared the generated values with those obtained from two groups of candidate titanium implant surfaces. For the first group (n = 5/group): grit-blasted acid etched (BAE), BAE with either discrete calcium phosphate crystal deposition or nanotube formation, machined titanium with nanotubes, or a nanofiber surface; each measured synergistic parameter was statistically compared with that of the resorbed bone surface and scored for inclusion in a "best fit" analysis. The analysis informed changes that could be made to a candidate implant surface to render it a closer "best fit" to that of the resorbed bone surface. In a second group of either titanium or titanium alloy implants their micro-topography, created by dual acid etching, was the same for each material substrate; but their nanotopographic complexity was changed by varying the degree of calcium phosphate crystalline deposits. These implants were also used in vivo where bone anchorage was tested using a tensile disruption test; and the "best fit" of synergistic parameters coincided with the best biological outcome for both titanium and titanium alloy implants. In conclusion, the four chosen synergistic parameters can be used to guide the sub-micron surface design of candidate implants, and our "best fit" approach is capable of identifying the surfaces with the best biological outcomes. © 2019 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 107B: 2165-2177, 2019.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".