Bibliographic record
Abstract
Osseointegrated implants are frequently used in reconstructive surgery, both in the dental and orthopedic field, restoring physical function and improving the quality of life for the patients. The bone anchorage is typically evaluated at micrometer resolution, while bone tissue is a dynamic composite material composed of nanoscale collagen fibrils and apatite crystals, with defined hierarchical levels at different length scales. In order to understand the bone formation and the ultrastructure of the interfacial tissue, analytical strategies needs to be implemented enabling multiscale and multimodal analyses of the intact interface. This paper describes a sample preparation route for successive analyses allowing assessment of the different hierarchical levels of interest, going from macro to nano scale and could be implemented on single samples. Examples of resulting analyses of different techniques on one type of implant surface is given, with emphasis on correlating the length scale between the different techniques. The bone-implant interface shows an intimate contact between mineralized collagen bundles and the outermost surface of the oxide layer, while bone mineral is found in the nanoscale surface features creating a functionally graded interface. Osteocytes exhibit a direct contact with the implant surface via canaliculi that house their dendritic processes. Blood vessels are frequently found in close proximity to the implant surface either within the mineralized bone matrix or at regions of remodeling.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".