In vivo 3D apical displacement of mandibular incisors and canines within the symphyseal region: A CBCT-based linear measurement technique (mm) — Validation study in five patients
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to develop and validate a technique that orthodontic clinicians and researchers could use to measure lower anterior teeth apical displacement through CBCT reconstructions. MATERIAL AND METHODS: This is an in vivo study in which lower incisors, canines, and symphyses of five randomly selected patients were three-dimensional (3D) reconstructed through an automatic segmentation with a manual refinement process. All the images were generated from 0.3mm voxel size cone-beam computed tomography (CBCT) imaging. A 3D coordinate system based on CBCT segmentation and landmarks allowed a spatial localization of the apices and quantification of their displacement during orthodontic treatment. The primary outcomes were the intra- and inter-reliabilities and the respective measurement errors. RESULTS: The overall results showcased good to excellent reliability for both intra- and inter-rater reliability analyses (9.91% and 7.33% measurement errors respectively). CONCLUSION: The proposed technique could be used for 3D measurements of the apical displacement of lower anterior teeth during orthodontic treatment. The identified measurement error of less than 10% is not clinically relevant in most orthodontic situations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".