The Role of Functional Parameters for Topographical Characterization of Bone‐Anchored Implants
Bibliographic record
Abstract
BACKGROUND: The surface topographical characterization of bone-anchored implants has been recommended to be based on amplitude, spatial, and hybrid parameters. There are also functional parameters that have the potential to describe characteristics important for a specific application. PURPOSE: The aim of the present study was to evaluate if parameters that have been described as functional in engineering applications are also relevant in the topographical characterization of bone-anchored implants. MATERIALS AND METHODS: The surface topography of threaded titanium implants with different surface roughness (S(a), S(ds), and S(dr)) was analyzed with an optical interferometer, and five candidating functional parameters (S(bi), S(ci), S(vi), S(m), and S(c)) were calculated. Examples of the same parameters for five commercially available dental implants were also calculated. Results The highest core fluid retention index (S(ci)) was displayed by the turned implants, followed by fixtures blasted with 250- and 25-microm particles, respectively. Fixtures blasted with 75-microm Al(2)O(3) particles displayed the lowest S(ci) value. This is the inverse order of the bone biological ranking based on earlier in vivo studies with the experimental surfaces included in the present study. CONCLUSION: A low core fluid retention index (S(ci)) seems favorable for bone-anchored implants. Therefore, it is suggested to include S(ci) to the set of topographical parameters for bone-anchored implants to possibly predict the biological outcome.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".