Where bone meets implant: the characterization of nano-osseointegration
Bibliographic record
Abstract
The recent application of electron tomography to the study of biomaterial interfaces with bone has brought about an awareness of nano-osseointegration and, to a further extent, demanded increasingly advanced characterization methodologies. In this study, nanoscale osseointegration has been studied via laser-modified titanium implants. The micro- and nano-structured implants were placed in the proximal tibia of New Zealand white rabbits for six months. High-resolution transmission electron microscopy (HRTEM), analytical microscopy, including energy dispersive X-ray spectroscopy (EDXS) and energy-filtered TEM (EFTEM), as well as electron tomography studies were used to investigate the degree of nano-osseointegration in two- and three-dimensions. HRTEM indicated the laser-modified surface encouraged the formation of crystalline hydroxyapatite in the immediate vicinity of the implant. Analytical studies suggested the presence of a functionally graded interface at the implant surface, characterized by the gradual intermixing of bone with oxide layer. Yet, the most compelling of techniques, which enabled straightforward visualization of nano-osseointegration, proved to be segmentation of electron tomographic reconstructions, where thresholding techniques identified bone penetrating into the nanoscale roughened surface features of laser-modified titanium. Combining high-resolution, analytical and three-dimensional electron microscopy techniques has proven to encourage identification and understanding of nano-osseointegration.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".