Fracture resistance of commonly used self-drilling orthodontic mini-implants
Bibliographic record
Abstract
OBJECTIVE: To investigate the fracture resistance of six commonly used self-drilling orthodontic mini-implants by comparing their respective fracture torques during insertion. MATERIALS AND METHODS: Ninety self-drilling mini-implants from six manufacturers (Aarhus, Dual-Top, OrthoEasy, Tomas-pin, Unitek, and VectorTAS), with diameters ranging from 1.4 to 1.8 mm, were inserted into acrylic blocks using a custom-made insertion device. Insertion torques were measured using a 6-degree-of-freedom load cell fixed to the base of the acrylic blocks, and peak torques experienced at the time of fracture for each of the mini-implants were recorded. One-way analysis of variance (α = .05) was used to compare the fracture torques among the six different groups. RESULTS: Statistical analysis revealed significant differences (P < .05) in the peak fracture torques among mini-implant groups. Mean fracture torques ranked as follows: Unitek (72 Ncm) > Tomas-pin (36 Ncm) > Dual-Top (32 Ncm) ≈ VectorTAS (31 Ncm) > OrthoEasy (28 Ncm) > Aarhus (25 Ncm), with significant differences found between all manufacturers, except for Dual-Top and VectorTAS. CONCLUSIONS: Mini-implants tested showed a wide range of torque at fracture depending on the manufacturer, with only a weak correlation between mini-implant diameter and fracture resistance. This torque should be considered at the time of mini-implant insertion to minimize the risk of implant fracture, especially in areas of high-density bone without predrilling.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".