Sella Turcica Bridging and Tooth Agenesis in Children With Unilateral Cleft Lip and Palate
Bibliographic record
Abstract
Aim: To investigate differences in sella turcica size and bridging in children with unilateral cleft lip and palate (UCLP) with or without concomitant dental anomalies. Patients and Methods: A cross-sectional study was carried out looking at 56 children with nonsyndromic UCLP. Lateral cephalograms, taken before alveolar bone grafting, were used to assess sella turcica height, width, area, and bridging. Panoramic radiographs were used to evaluate the presence of dental anomalies in the cleft area including agenesis, supernumerary, and peg-shaped lateral incisors. Differences between sella turcica measurements in the presence or absence of dental anomalies were assessed using t tests. Differences between the prevalence of dental anomalies and sella turcica bridging were assessed using χ 2 and Fisher exact tests. Multinomial logistic regression was used to investigate potential associations between sella measurements and dental anomalies. Results: Twenty-six of the 56 children presented with agenesis of the cleft-side lateral incisor, while 7 had a supernumerary, and 19 had a peg-shaped lateral incisor. With regard to sella turcica bridging, 27 children had no calcification, 25 partial and 4 complete calcification. Children with agenesis of the cleft-side lateral incisor showed a shorter sella maximum height ( P = .010) and a smaller area ( P = .019). When looking at sella turcica bridging, 100% of children with complete calcification showed agenesis of the cleft-side lateral incisor, compared with 52% and 33% of children with partial calcification and no calcification, respectively ( P = .034). Conclusions: Children with UCLP and sella turcica bridging are more likely to present with agenesis of the cleft-side maxillary lateral incisor.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".