Cone‐Beam CT for Preoperative Implant Planning in the Posterior Mandible: Visibility of Anatomic Landmarks
Bibliographic record
Abstract
BACKGROUND: The technical development has given a new type of modality, cone-beam computed tomography (CBCT). This technique has a high potential to solve different diagnostic problems among which is preoperative planning for implants in the posterior mandible. PURPOSE: The aim of this retrospective study was to evaluate the visibility of the mandibular canal and the marginal bone crest and the agreement between observers in images from one CBCT technique. MATERIALS AND METHODS: Thirty consecutive patients were examined with 3D Accuitomo (J. Morita Mfg. Corp., Kyoto, Japan) in one side of the mandible, where the second premolar and molars were lost. The examined volume was 30 by 40 mm. Seven observers evaluated the visibility and the location of the mandibular canal and the marginal crest by visually deciding if the structures were clearly visible, probably visible, or invisible in one cross-sectional image, approximately 1 cm posterior to the mental foramen. In a later session, the observers also marked the two anatomic structures. If the decision was not "clearly visible" or if the anatomic structures were difficult to identify, the observers had to use other cross-sectional, axial, and/or sagittal images in the volume. RESULTS: The confidence among the observers evaluating the marginal bone crest was high. Two observers never used any other images, and the rest took help in two to seven cases. When marking the mandibular canal, the observers, in general, used more images. In five cases (17%), all the observers only used the single cross-sectional image. The agreement on the position of the canal was also high. CONCLUSION: With this CBCT modality (3D Accuitomo), the visibility of the mandibular canal and the marginal crest, as well as the observer agreement of the location of these structures, was high. Hence, the 3D Accuitomo can be recommended for implant planning in the posterior mandible.
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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 | 0.004 |
| 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".