Reliability of Traditional Cephalometric Landmarks as Seen in Three-Dimensional Analysis in Maxillary Expansion Treatments
Bibliographic record
Abstract
OBJECTIVE: To evaluate intra-examiner and inter-examiner reliability of 3D CBCT-generated landmarks previously used in traditional 2D cephalometry. MATERIALS AND METHODS: Twenty-four CBCTs NewTom 3G (Aperio Services, Verona, Italy) were randomly selected from patients participating in a clinical trial involving maxillary expansion treatments. The principal investigator located the landmarks five times, and four other investigators located the same landmarks once. Intra-examiner and inter-examiner reliability values were determined using intraclass correlation coefficients (ICCs). To assist in interpretation of the clinical significance of landmark identification differences, average mean differences for x, y, and z landmark coordinates were determined from the repeated assessments. Landmarks then were separated into groups with respect to the region they represented and then were compared via repeated measures ANOVA and multiple comparisons via Bonferroni corrected alpha. RESULTS: Intra-examiner and inter-examiner reliability for x, y, and z coordinates for all landmarks were acceptable, all being greater than 0.80. Most of the mean measurement differences obtained from trials within the principal investigator in all three axes were less than 1.5 mm. Inter-examiner mean measurement differences generally were larger than the intra-examiner differences. CONCLUSIONS: Based on this, the best landmarks for use in verifying expansion treatment results are Ekm, buccal surface, and apexes of upper molars, upper premolars and upper canines, and buccal surfaces of lower molars and lower canines. Foramen Spinosum, ELSA, Auditory External Meatus, and Dorsum Foramen Magnum demonstrated adequate reliability for determining a standardized reference system.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".