Agreement of in vitro orthodontic measurements on dental plaster casts and digital models using Maestro 3D ortho studio software
Bibliographic record
Abstract
OBJECTIVE: Diagnostic casts are one of the standard components of orthodontic records. But they have several drawbacks such as the need for physical space for storage and the risk of breaking due to their brittle composition. Today, the digitalization of orthodontic models is a progress in orthodontics. The purpose of this study was to compare and evaluate common orthodontic linear measurements on plaster casts and digital 3D models using Maestro 3D ortho studio® scanner and software (AGE Solutions®, Pontedera, Italy). MATERIALS AND METHODS: Study casts of 30 orthodontic patients were selected. Tooth width, space analysis, Bolton analysis, overjet, overbite, and linear measurements of dental arch dimensions were performed by two examiners on plaster casts and digital models. STATISTICAL ANALYSIS: Intra- and interexaminer agreements were evaluated in both manual and digital methods and paired t test was used for evaluating the agreement between the manual and digital measurement. The significance level was set at 0.05. RESULTS: The intraexaminer agreement was excellent (ICC > 0.75) for most variables in both manual and digital methods. The correlation between the two examiners was significant (p < .05) for most manual and digital measurements. The differences between the manual and digital measurements, although maybe statistically significant, were not clinically significant for most variables. CONCLUSION: The use of "Maestro 3D" (AGE Solutions, Pontedera, Italy) scanner and software was acceptable for orthodontic diagnostic measurements instead of study casts.
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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 | 0.012 | 0.018 |
| 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 | 0.002 |
| 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".