Effects of Drilling Technology on Mini-Implant Primary Stability: A Comparison of the Mechanical Drilling and Femtosecond Laser Ablation
Bibliographic record
Abstract
Objectives: Primary stability is a fundamental prerequisite in predicting the prognosis of a mini-implant (MI) as a skeletal anchorage. This study aims to evaluate the influence of implant site preparation technology on the primary stability of MI. Methods: A total of 108 bovine cortical bone samples were fabricated to three thicknesses (0.5, 1.0, and 1.5 mm). For each thickness group, the samples were divided into three subgroups: I (without site preparation), II (site preparation with a mechanical drill), and III (site preparation with femtosecond laser ablation). After MI insertion into these samples, the pull out strength of MI was measured by lateral pull out tests. Results: For the 0.5 mm thickness samples, the lateral pull-out strength was 9.9±2.7 N in subgroup I, 6.7±2.1 N in subgroup II, and 15.2±2.6 N in subgroup III. For the 1.0 mm thickness samples, the lateral pull-out strength was 39.3±2.5N in subgroup I, 38.2±2.7N in subgroup II, and 46.3±1.7 N in subgroup III. For the 1.5 mm thickness samples, the lateral pull-out strength was 73.9±4.8 N in subgroup I, 70.1±2.8 N in subgroup II, 75.0±2.2 N in subgroup III. No signs of carbonization or substantial cracking were visible in any of the bone samples. Conclusion: Site preparation with laser ablation significantly improved the lateral pull-out strength over mechanical preparation and control (no site preparation) in thinner cortical bone samples (1.0 and 0.5 mm). Such improvement in lateral pull-out strength decreases as the samples become thicker and diminishes in thick (1.5 mm) cortical bone samples.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".