Bibliographic record
Abstract
OBJECTIVE: To describe erupting maxillary canine positions in patients with bone-grafted alveolar clefts. SAMPLE: The sample consisted of 101 cleft sites from patients with complete unilateral or bilateral cleft lip and palate who had early (< or =9 years) or late (>9 years) secondary alveolar bone grafts. METHODS: Canine position was assessed using panoramic radiographs taken before and after alveolar bone grafts. Vertical canine positions were assessed using the long axis of the maxillary permanent canine relative to a 90 degrees vertical reference line. Lateral canine positions were defined using the relationship between the canine tip and the midplane of the lateral incisor root. Anomalous lateral incisors were recorded. Statistical analysis included Student's t tests and chi-square tests. RESULTS: Patients with alveolar clefts had a 20-fold increased risk for canine impaction, based on erupting canine positions. Abnormal vertical canine positions decreased following early and late alveolar bone grafts (p < .05), whereas abnormal lateral canine positions increased following late alveolar bone grafts (p < .01). Of the cleft sites with altered canine positions, 61% also had a lateral incisor anomaly. Based on canine position, the non-cleft-side canine had the same risk for impaction as the cleft-side canine. CONCLUSIONS: Patients with alveolar clefts have a significantly higher risk for canine impaction compared with patients without clefts. Timing of alveolar bone grafts and lateral incisor anomalies influenced the risk for canine impaction. An alveolar bone graft should be planned in accordance with maxillofacial development, including the eruption of teeth adjacent to the cleft.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".