A novel technique for measurement of orthodontic mini-implant stability using the Osstell ISQ device
Bibliographic record
Abstract
OBJECTIVES: To develop and validate a method for application of the Osstell ISQ device in the assessment of mini-implant stability. MATERIALS AND METHODS: An adaptor was developed for attachment of Osstell's SmartPeg onto a variety of orthodontic mini-implants. For validation of the adaptor, Benefit mini-implants were inserted into bone blocks that mimicked different stability conditions. The Osstell device was used to assess mini-implant stability with the adaptor (test measurement) and conventional SmartPeg attachment (gold-standard measurement). Implant stability quotient (ISQ) values were assessed for agreement, repeatability, and reproducibility. RESULTS: Strong positive correlations were found between ISQ values obtained using the novel adaptor and the conventional attachment. Repeatability and reproducibility of ISQ values with the adaptor were similar to those obtained with the conventional attachment. CONCLUSIONS: A method was developed and validated to assess the stability of orthodontic mini-implants using the Osstell system. The novel mini-implant adaptor provided repeatable and reproducible measurements of mini-implant stability, which agreed with those obtained using a conventional SmartPeg attachment. This adaptor permits noninvasive stability assessment of various designs of mini-implants, most of which are incompatible with the conventional SmartPeg attachment.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".