Comparison of mandibular first molar mesial root canal morphology using micro-computed tomography and clearing technique
Bibliographic record
Abstract
OBJECTIVE: Micro-computed tomography (MCT) with alternative image reformatting techniques shows complex and detailed root canal anatomy. This study compared two-dimensional (2D) and 3D MCT image reformatting with standard tooth clearing for studying mandibular first molar mesial root canal morphology. MATERIALS AND METHODS: Extracted human mandibular first molar mesial roots (n=31) were scanned by MCT (Skyscan 1172). 2D thin-slab minimum intensity projection (TS-MinIP) and 3D volume rendered images were constructed. The same teeth were then processed by clearing and staining. For each root, images obtained from clearing, 2D, 3D and combined 2D and 3D techniques were examined independently by four endodontists and categorized according to Vertucci's classification. Fine anatomical structures such as accessory canals, intercanal communications and loops were also identified. RESULTS: Agreement among the four techniques for Vertucci's classification was 45.2% (14/31). The most frequent were Vertucci's type IV and then type II, although many had complex configurations that were non-classifiable. Generally, complex canal systems were more clearly visible in MCT images than with standard clearing and staining. Fine anatomical structures such as intercanal communications, accessory canals and loops were mostly detected with a combination of 2D TS-MinIP and 3D volume-rendering MCT images. CONCLUSIONS: Canal configurations and fine anatomic structures were more clearly observed in the combined 2D and 3D MCT images than the clearing technique. The frequency of non-classifiable configurations demonstrated the complexity of mandibular first molar mesial root canal anatomy.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".