Frequency and Pattern of Anterior Crossbite With Primary and Mixed Dentition in School Children
Bibliographic record
Abstract
OBJECTIVES To determine the frequency and pattern of anterior crossbite with primary and mixed dentition in School Children. METHODOLOGY A descriptive cross-sectional study was conducted at Sharif Medical and Dental College, Lahore. This study included 296 participants having either deciduous or mixed dentition, no history of orthodontic treatment, aged between 3-11 years, both genders and Pakistani nationals. Participants with a history of trauma, cleft lip/palate, or any craniofacial syndrome and systemic disease were excluded. Participant’s age, gender, skeletal class, and anterior crossbite (ACB) were recorded. The Chi-square/Fisher exact test was run to compare ACB and their pattern among gender, age group, and skeletal class. RESULTSThere is a relatively high rate of anterior crossbite in this population, which is about 10%. The females were 169(57.09%) and males were 127(42.91%). The mean age was 6.92 ± 1.68 years. Overall, the ACB was present in 31(10.47%). The most common pattern of ACB was single incisor involvement (n=11, 35.48%) followed by two incisors (n=9, 29.03%), and the least was four incisors (n=5, 16.13%). The difference for ACB was statistically significant among skeletal classes (p<0.001). The frequency of ACB was higher in skeletal class 1 (n=17, 54.84%) and in skeletal class 3 (n=13, 41.94%) than in class 2 (n=1, 3.23%). CONCLUSION The frequency of anterior crossbite is about 10%, which is relatively higher than in other populations. Most anterior cross bites are dental due to one or two incisor involvement, which can be corrected easily at the mixed dentition stage.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".