Cone-beam computed tomographic reconstructions in the evaluation of maxillary impacted canines
Bibliographic record
Abstract
Purpose: Prior to cone-beam computed tomography (CBCT), orthodontic competence included radiological interpretation. Nevertheless, maxillary impacted canines (MICs), because of their position and adjacent complex anatomy, have been challenging to interpret, particularly with regard to root resorption. Although CBCT cross-sectional reconstructions of MICs yielded clearer insights into its diagnosis and treatment planning, the value of simultaneously using 2 different cross-sectional or multiplanar reconstructions of the CBCT datasets - orthogonal and curved/panoramic multiplanar reconstructions - has hitherto not been considered. Materials and Methods: Both orthogonal and curved/panoramic multiplanar reconstruction series of 5 screenshots were each reconstructed from the 5 cm × 5 cm CBCT datasets of 15 separate MICs. Fifteen credentialled and experienced orthodontist volunteers reviewed 2 separate PowerPoints of 15 randomized series each, 1 week apart. Their review considered 6 factors that could affect treatment: the position and level of the MIC, the presence or absence of root resorption, ankylosis, cysts, and dilaceration. Results: All 15 orthodontists were statistically similar regarding overall years of experience and of CBCT use. Although either reconstruction alone allowed the orthodontists to determine whether ankylosis and, to a lesser extent, most of the other features were present or absent in the MIC, reviewing both reconstructions together was necessary to determine whether root resorption was present or absent in the adjacent tooth. Conclusion: Reviewing both orthogonal and curved/panoramic multiplanar reconstructions was necessary to evaluate the presence or absence of root resorption in the teeth adjacent to MICs and that of many other features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".