Sensitivity analysis for plane orientation in three-dimensional cephalometric analysis based on superimposition of serial cone beam computed tomography images
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to evaluate the potential errors associated with superimposition of serial cone beam CT (CBCT) images utilizing reference planes based on cranial base landmarks using a sensitivity analysis. METHODS: CBCT images from 62 patients participating in a maxillary expansion clinical trial were analysed. The left and right auditory external meatus (AEM), dorsum foramen magnum (DFM) and the midpoint between the left and right foramen spinosum (ELSA) were used to define a three-dimensional (3D) anatomical reference co-ordinate system. Intraclass correlation coefficients for all four landmarks were obtained. Transformation of the reference system was carried out using the four landmarks and mathematical comparison of values. RESULTS: Excellent intrareliability values for each dimension were obtained for each landmark. Evaluation of the method to transform the co-ordinate system was first done by comparing interlandmark distances before and after transformations, giving errors in lengths in the order of 10-14% (software rounding error). A sensitivity evaluation was performed by adding 0.25 mm, 0.5 mm and 1 mm error in one axis of the ELSA. A positioning error of 0.25 mm in the ELSA can produce up to 1.0 mm error in other cranial base landmark co-ordinates. These errors could be magnified to distant landmarks where in some cases menton and infraorbital landmarks were displaced 4-6 mm. CONCLUSIONS: Minor variations in location of the ELSA, both the AEM and the DFM landmarks produce large and potentially clinically significant uncertainty in co-ordinate system alignment.
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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 | 0.013 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 | 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".