A Comparison of the Mechanical Measures Used for Assessing Orthodontic Mini-Implant Stability
Bibliographic record
Abstract
PURPOSE: Mechanical loosening remains a common complication associated with mini-implant failure. The purpose of this study was to compare common mechanical measures of mini-implant stability to determine their association and reliability. MATERIALS AND METHODS: Ninety self-drilling orthodontic mini-implants from 6 manufacturers were inserted into artificial bone blocks. Insertion torques (ITs) and Periotest values (PVs) were measured. Subsequently, mini-implants underwent pull-out testing for measures of pull-out load (POL) and screw displacement (ScrD). Stability measurements were compared using one-way ANOVA, associations among them were assessed using correlation analyses, and reliability was evaluated using coefficients of variation (COVs). RESULTS: Variations in stability of mini-implants were found, specific to the mechanical measure used for assessment (P < 0.05). The strongest correlations were found between IT and PV (r = -0.68) and between IT and POL (r = 0.66). Overall, PV showed the greatest variability (COV: 11%-100%) compared with IT (≤11%), POL (≤4%), and ScrD (≤19%). CONCLUSIONS: IT, PV, and POLs only agreed moderately in their assessment of mini-implant stability, and Periotest showed the least reliability in predicting mini-implant stability. As such, independent and interchangeable use of these stability measures should be avoided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".