Three‐dimensional modelling and concurrent measurements of root anatomy in mandibular first molar mesial roots using micro‐computed tomography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 it