Linear Measurement Accuracy of Eight Cone Beam Computed Tomography Scanners
Bibliographic record
Abstract
BACKGROUND: Information regarding linear accuracy is necessary for efficient treatment evaluation, especially for maxillofacial reconstruction or implants. PURPOSE: To investigate the accuracy of linear measurements from multiple cone beam computed tomography (CBCT) devices. MATERIALS AND METHODS: A RANDO® phantom was scanned with eight CBCT scanners (11 modes). The viewing software accompanying each scanner was employed for measurements in mediolateral, anteroposterior, and supero-inferior dimensions by two dentomaxillofacial radiologists. Digital caliper measurements were used as a "gold standard." ANOVA with Scheffé post hoc analysis and intraclass correlation coefficient (ICC) were utilized for statistical analyses. The level of confidence was 95%. RESULTS: Differences from the gold standard among 11 acquisition modes were statistically significant (p < .001). Measurements from one unit were always underestimated compared with all others (p < .001). The range of absolute measurement errors for tested units was -2.56 to 0.54 mm (mean ± SD 0.45 ± 0.71) including the outlier and -0.34 to 0.54 mm (0.16 ± 0.11) excluding the outlier. Slightly more values were underestimated than overestimated (41 of 66 measurements, 7 out of 11 CBCT modes). ICC scores for inter- and intraobserver agreement were perfect (1.000). CONCLUSIONS: Treatment planning from large-volume CBCT was found to be reliable in all except one of the investigated scanners. New CBCT scanners should always be tested for geometric accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".