New laser-treated implant surfaces: A histologic and histomorphometric pilot study in rabbits
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to confirm the validity of laser treated implant surfaces, with regard to high superficial purity preservation and to extremely regular and uniform roughness surfaces. METHODS: In this in vivo study, seven different laser treated implant surfaces were analyzed. A diode-pumped solid state source laser, in a Q-Switch output mode, was used at various wavelengths, which were chosen to generate surface irregularities of varying diameter, depth and pitch. Twenty one implants were placed in 11 New Zealand rabbits. Eight weeks after surgery, implants were harvested for histometric analysis: total, threads and body bone-to-implant, and bone-to-implant contacts were measured. The morphologic analysis of the surface was carried out using a Scanning Electron Microscope. RESULTS: Average bone-implant contact values were approximately 50% for all tested surfaces. Both total and threads values, within the same processing pattern group, had a high variance. Bone-implant contact thread and body variances were different, so that is possible that laser beam angle is able to modify the superficial roughness and thus the histological response. CONCLUSIONS: Implants provided with pores of 20 and 25 µm achieved more than satisfactory bone-implant contact partial peaks. Further statistically significant experiments are needed in order to study, in depth, these surfaces.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".