A Comparative Study of Computed Tomography and Magnetic Resonance Imaging for the Detection of Mandibular Canals and Cross‐Sectional Areas in Diagnosis prior to Dental Implant Treatment
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Computed tomography (CT) is effective in the diagnosis of dental implants. However, it has the disadvantage of exposing patients to high doses of x-rays, and the mandibular canals cannot be detected by CT in some clinical cases. PURPOSE: The purpose of this study was to examine the detectability of the anatomic morphology of the molar region in the lower jaw (where implantation is common) by CT and magnetic resonance imaging (MRI), to compare the data, and to determine the usefulness of MRI in diagnosis prior to dental implant treatments. MATERIALS AND METHODS: Eleven female subjects (average age, 59 years) who had partially edentulous mandibles (total of 19 sites) were included in the study. CT and MRI were performed with the same subjects, and the degrees of identification of the mandibular canal in the first and second molar regions were compared. Dimensional accuracy in the second molar region was also compared. RESULTS: With CT, the canals of the first molar regions were not identified in 11 of 19 sites; however, MRI identified the canals in all 19 sites. Using the kappa index, we found that the inter- and intraobserver identification reliabilities (0.84 and 0.87, respectively) were excellent, especially for MRI. Dimensional positioning of the canal in the second molar region was almost the same with MRI as with CT. CONCLUSIONS: MRI is an alternative method in diagnosis prior to dental implant treatment in the mandibular molar region.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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