A CBCT Investigation of the Association between Sella-Turcica Bridging and Maxillary Palatal Canine Impaction
Bibliographic record
Abstract
Objectives. To investigate the association between unilateral/bilateral maxillary canine impaction and sella-turcica bridging using CBCT imaging. Methods. This retrospective comparative study analyzed 76 CBCT images of the craniofacial complex including sella-turcica. The impacted cuspid group consisted of thirty-eight subjects (7 males, 31 females; mean age, 14.6 ± 3.2 years) diagnosed with unilateral (left n=14 , right n=11 ) or bilateral ( n=13 ) palatal canine impaction. The control group included thirty-eight subjects matched by sex (7 males, 31 females; mean age, 19.5 ± 3.6 years) with no impaction. Multinomial logistic regression analysis was used to determine the association between unilateral/bilateral canine impaction and right and left sella-turcica bridging. Results. The prevalence of sella-turcica bridging was 59.3% and 50% in the impacted canine and control groups, respectively. Although the odds for unilateral canine impaction were increased in the right and left sella-turcica bridging groups compared to the controls, the difference was not statistically significant. The risk of bilateral impaction was different between the two sides of sella-turcica bridging, but, again, the findings were not statistically significant. Conclusion. Contrary to previous 2D studies, there is no statistically significant association between unilateral/bilateral palatal canine impaction and sella-turcica bridging when using 3D CBCT.
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".