Accuracy of laser-scanned models compared to plaster models and cone-beam computed tomography
Bibliographic record
Abstract
OBJECTIVE: To compare the accuracy of measurements obtained from the three-dimensional (3D) laser scans to those taken from the cone-beam computed tomography (CBCT) scans and those obtained from plaster models. MATERIALS AND METHODS: Eighteen different measurements, encompassing mesiodistal width of teeth and both maxillary and mandibular arch length and width, were selected using various landmarks. CBCT scans and plaster models were prepared from 60 patients. Plaster models were scanned using the Ortho Insight 3D laser scanner, and the selected landmarks were measured using its software. CBCT scans were imported and analyzed using the Avizo software, and the 26 landmarks corresponding to the selected measurements were located and recorded. The plaster models were also measured using a digital caliper. Descriptive statistics and intraclass correlation coefficient (ICC) were used to analyze the data. RESULTS: The ICC result showed that the values obtained by the three different methods were highly correlated in all measurements, all having correlations>0.808. When checking the differences between values and methods, the largest mean difference found was 0.59 mm±0.38 mm. CONCLUSIONS: In conclusion, plaster models, CBCT models, and laser-scanned models are three different diagnostic records, each with its own advantages and disadvantages. The present results showed that the laser-scanned models are highly accurate to plaster models and CBCT scans. This gives general clinicians an alternative to take into consideration the advantages of laser-scanned models over plaster models and CBCT reconstructions.
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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 | 0.025 |
| 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) | 0.002 | 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".