Measurement accuracy and reliability of tooth length on conventional and CBCT reconstructed panoramic radiographs
Bibliographic record
Abstract
INTRODUCTION: This in vivo study assessed accuracy and reliability of tooth length measurements obtained from conventional panoramic radiographs and CBCT panoramic reconstructions to that of a digital caliper (gold standard). METHODS: The sample consisted of subjects who had CBCT and conventional panoramic radiographic imaging and who required maxillary premolar extraction for routine orthodontic treatment. A total of 48 teeth extracted from 26 subjects were measured directly with digital calipers. Radiographic images were scanned and digitally measured in Dolphin 3D software. Accuracy of tooth length measurements made by CBCT panoramic reconstructions, conventional panoramic radiographs and digital caliper (gold standard) were compared to each other by repeated measures one-way ANOVA with Bonferroni correction and by single measures intraclass correlation coefficient. RESULTS: Repeated root length measures with digital calipers, panoramic radiographs and CBCT constructed panoramic-like images were all individually highly reliable. Compared to the caliper (gold standard), tooth measurements obtained from conventional panoramic radiographs were on average 6.3 mm (SD = 2.0 mm) longer, while tooth measurements from CBCT panoramic reconstructions were an average of 1.7 mm (SD = 1.2 mm) shorter. CONCLUSIONS: In comparison to actual tooth lengths, conventional panoramic radiographs were relatively inaccurate, overestimating the lengths by 29%, while CBCT panoramic reconstructions underestimated the lengths by 4%.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".