The remaining dentin thickness investigation of the attempt to remove broken instrument from mesiobuccal canals of maxillary first molars with virtual simulation technique
Bibliographic record
Abstract
BACKGROUND: To investigate differences in the estimated minimum remaining dentin thickness (RDT) between periapical radiographs using the paralleling and parallax technique, after simulated removal of broken instrument from the mesiobuccal (MB) canal of maxillary first molar in virtual simulation model. The 3D measurement was taken as the standard for comparison. METHODS: Thirty-six maxillary first molars were scanned by micro-CT and reconstructed as 3-dimensional (3D) model. A virtual fragment of an instrument was created within the MB canal in software. Removal of the broken instrument was simulated in both the 3D and 2D dataset. Then, the models of all specimens were submitted to 2D and 3D measurements for the lowest (RDT) value in each. Differences in the values between the paralleling and parallax radiographic technique and the 3D-RDT value were analyzed with two-way Analysis of Variance. The Intra-class Correlation Coefficient (ICC) was used to assess consistency of the RDT measurements between the two periapical radiographic and techniques and 3D analysis. RESULTS: There was significant difference between RDT value obtained from the paralleling technique and 3D-RDT. There were no differences between RDT obtained from parallax (angled) technique and 3D-RDT. The ICC of RDT values between paralleling technique and 3D measurement were lower than 0.75. ICC between angled radiographs and 3D technique was close to 0.75. The optimal horizontal angle for the parallax technique was about 21°. CONCLUSIONS: The virtual simulation technique can provide valuable insight into the benefit/risk analysis before removal of a broken instrument. Parallel radiographs overestimate the actual remain dentin thickness in mesiobuccal canals of maxillary first molars, whereas the parallel technique would give a closer estimate to the actual thickness at a projection angle of about 21°.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".