Accuracy and reliability of craniometric measurements on lateral cephalometry and 3D measurements on CBCT scans
Bibliographic record
Abstract
OBJECTIVE: To compare the accuracy of craniometric measurements made on lateral cephalograms and on cone beam computed tomography (CBCT) images. MATERIALS AND METHODS: Ten fiducial markers were placed on known craniometric landmarks of 25 dry skulls with stable occlusions. CBCT scans and conventional lateral headfilms subsequently were taken of each skull. Direct craniometric measurements were compared with CBCT measurements and with cephalometric measurements using repeated measures analysis of variance (ANOVA). All measurements were repeated within a 1-month interval, and intraclass correlations were calculated. RESULTS: No statistically significant difference was noted between CBCT measurements and direct craniometric measurements (mean difference, 0.1 mm). All cephalometric measurements were significantly different statistically from direct craniometric measurements (mean difference, 5 mm). Significant variations among measurements were noted. Some measurements were larger on the lateral cephalogram and some were smaller, but a pattern could be observed: midsagittal measurements were enlarged uniformly, and Co-Gn was changed only slightly; Co-A was always smaller. CONCLUSION: CBCT craniometric measurements are accurate to a subvoxel size and potentially can be used as a quantitative orthodontic diagnostic tool. Two-dimensional cephalometric norms cannot be readily used for three-dimensional measurements because of differences in measurement accuracy between the two exams.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".