Non-syndromic hypodontia of maxillary lateral incisors and its association with other dental anomalies
Bibliographic record
Abstract
BACKGROUND: Tooth agenesis (TA) is the developmental absence of one or more teeth and is the most common craniofacial disorder in humans. Maxillary lateral incisor agenesis (MLIA) is a specific subtype of TA and can have esthetic, functional, and psychosocial implications for patients. The aim of this study was to evaluate the prevalence of MLIA amongst patients with non-syndromic tooth agenesis, as well as its association with other dental anomalies. MATERIALS AND METHODS: The dental records of 240 patients with non-syndromic congenitally missing teeth treated at the University of Alabama at Birmingham Department of Orthodontics were reviewed. Dolphin Imaging software was used to identify missing teeth, microdonts, peg laterals, impactions, and transpositions. Data were analyzed using chi-square or Fisher's exact test. All the tests were two-sided at the significance level of 0.05 (SAS 9.4). RESULTS: In the patient cohort, MLIA prevalence was 37.5% (second most common) and no gender or ethnic differences were identified. We also observed the bilaterally missing lateral incisors more frequently than the unilateral presentation (p = 0.0006). Additionally, 62.5% of patients with unilateral MLIA displayed a contralateral tooth that was a peg (p = 0.0001); however, no association was found with other microdonts. Furthermore, of the 90 patients missing at least one maxillary lateral incisor, 42.2% were missing another tooth type and 10% of MLIA patients also had an impacted tooth (mainly maxillary canines). However, these were not statistically significant. Finally, no transposed teeth were found in our patients. CONCLUSIONS: This study found that maxillary lateral incisors were the second most frequently missing teeth. When clinicians diagnose congenital absence of a maxillary lateral incisor, the patient should be evaluated for other missing teeth, peg lateral incisors, or potential impactions, especially maxillary canines.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".