Identification, morphometry and volumetric analysis of the genial tubercles in radiographic and tomographic examinations
Bibliographic record
Abstract
Abstract Purpose: To determine the identification, morphometry and volumetric analysis of genital tubercles in radiographic and tomographic examinations. Methods: 15 dry human jaws were exposed to four stages. At first, they were exposed to two-dimensional radiographic and three-dimensional image examinations. In the second stage, the same image exams were performed on the mandibles with the genial tubers accentuated with barium sulphate. In the third stage, a gingival needle was inserted in the lingual foramen to perform the imaging exams. In the last stage, the genial tubercles were removed for imaging. The radiographs were evaluated by two radiologist dentists trained for morphological analysis. For the morphometric evaluation, the software ITK-snap® Version 3.6.0 was used. Results: As a result of the periapical radiographs, no changes were observed in the initial or final images. In addition, with the highlight of barium sulphate, it is noticed that the genial tubers increase the radiopacity of the symphysis region. In the occlusal radiographic images, changes were observed in each of the stages, since the position of the mandible during the examination shows the projections of the genial tubercles, and the two-dimensional examination is indicated for the evaluation of these anatomical structures. Conclusion: Cone beam computed tomography has, in a different way, the property of providing three-dimensional images rich in detail. Thus, it can be concluded that the periapical radiography of the lower incisors highlights the lingual canal, the occlusal radiography highlights the genial tubercles and tomography is the exam that presents the conditions to differentiate these structures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".