Reliability of anatomic structures as landmarks in three-dimensional cephalometric analysis using CBCT
Bibliographic record
Abstract
OBJECTIVE: To identify anatomic structures in three dimensions and examine their reliability to be used as landmarks in a three-dimensional coordinate cephalometric analysis, using cone-beam computerized tomography (CBCT). MATERIALS AND METHODS: Thirty CBCT images were randomly selected for landmark location. Forty-two anatomic landmarks, which are not included in the traditional cephalometric landmarks, were chosen based on radiographic characteristics that make them pragmatic to mark in the CBCT image slices. The principal investigator marked the full set of landmarks on the software by navigating in the X, Y, and Z axes for every image three times, with each measurement trial being at least 1 week apart. One other investigator also located the landmarks once for each image for reliability purposes. Intraclass correlation coefficients (ICCs) were used to analyze the mean differences in landmark location in all axes. RESULTS: Intra- and interexaminer reliability for x, y, and z coordinates for all landmarks had ICC greater than 0.95 with confidence interval of 0.88-0.99. Mean measurement differences found were <1.4 mm for all landmarks in all three coordinates. Mean measurement error differences obtained in the principal investigator's trials were primarily <0.5 mm. CONCLUSION: The most reliable and reproducible landmarks tested for use in CBCT are mental foramina, infraorbital foramina, inferior hamulus, dens axis, foramina transversarium of atlas, medial and lateral condyles of the mandible, superior clinoid processes, and mid-clinoid.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".