Surface Damage on Dental Implants with Release of Loose Particles after Insertion into Bone
Bibliographic record
Abstract
BACKGROUND: Modern dental implants present surface features of distinct dimensions that can be damaged during the insertion procedure into bone. PURPOSE: The aims of this study were (1) to quantify by means of roughness parameters the surface damage caused by the insertion procedure of dental implants and (2) to investigate the presence of loose particles at the interface. MATERIALS AND METHODS: Three groups of dental implants representing different surface topographies were inserted in fresh cow rib bone blocks. The surface roughness was characterized by interferometry on the same area before and after the insertion. Scanning electron microscopy (SEM)-back-scattered electron detector (BSD) analysis was used to identify loose particles at the interface. RESULTS: The amplitude and hybrid roughness parameters of all three groups were lower after insertion. The surface presenting predominance of peaks (Ssk [skewness] > 0) associated to higher structures (height parameters) presented higher damage associated to more pronounced reduction of material volume. SEM-BSD images revealed loose titanium and aluminum particles at the interface mainly at the crestal cortical bone level. CONCLUSIONS: Shearing forces during the insertion procedure alters the surface of dental implants. Loose metal particles can be generated at bone-implant interface especially around surfaces composed mainly by peaks and with increased height parameters.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".