Three‐dimensional modelling and concurrent measurements of root anatomy in mandibular first molar mesial roots using micro‐computed tomography
Bibliographic record
Abstract
AIM: To obtain concurrent radicular measurements in the mesiobuccal (MB) and mesiolingual (ML) canals of mandibular first molars using scanned data of micro-computed tomography (μCT) with novel software. METHODOLOGY: The scanned data from 37 mandibular first molar mesial roots were reconstructed and analysed with custom-developed software (Kappa2). For each canal, three-dimensional (3D) surface models were re-sliced at 0.1-mm intervals perpendicular to the central axis. Dentine thicknesses, canal widths and 3D curvatures were measured automatically on each slice. Measurements were analysed statistically with anova for differences at each direction and at different levels of both canals. RESULTS: Lateral dentine thicknesses were significantly higher than mesial and distal thicknesses, at all the levels of both canals (P < 0.001). Mesial thicknesses were significantly higher than distal thicknesses in the coronal third of both canals (P < 0.001). Thinnest dentine thicknesses were mainly located on the disto-inside of both canals. Narrowest canal widths were 0.24 ± 0.10 and 0.22 ± 0.09 mm in MB and ML canals, respectively. Canal curvatures were greatest in the apical third of both canals (P < 0.001), and they were greater in the MB canals than in the ML canals (P < 0.05). CONCLUSIONS: Micro-computed tomography with novel software provided valuable anatomical information for optimizing instrumentation and minimizing mishaps in nonsurgical root canal treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".