New Neonatal Classification of Unilateral Cleft Lip and Palate Part 2: To Predict Permanent Lateral Incisor Agenesis and Maxillary Growth
Bibliographic record
Abstract
Objectives : To bring a neonatal classification system of unilateral cleft lip and palate and to correlate this classification with the distribution of the permanent lateral incisor and maxillary growth. Design : Retrospective with longitudinal follow-up. Setting : Tertiary. Patients : A total of 112 individuals with treated unilateral cleft lip and palate and 30 controls. Main Outcome Measures : Unilateral cleft lip and palate neonatal casts were classified anatomically in four categories, in which Class 1 corresponds to a maxillary arch with a narrow alveolar cleft; Class 2 corresponds to a balanced form; Class 3 corresponds to a wide cleft and short maxilla; and Class 4 corresponds to a wide cleft and long maxilla. The classification was correlated with the distribution of the permanent lateral incisor. Maxillary growth was evaluated using a cephalometric analysis after the age of 10 years. Results : Clinical classification of unilateral cleft lip and palate found 10 cases of Class 1 (8.9%), 34 cases of Class 2 (30.4%), 46 cases of Class 3 (41.1%), and 22 cases of Class 4 (19.6%). The permanent lateral incisor was most often present in narrower clefts (Classes 1 and 2); whereas, large clefts (Classes 3 and 4) were relatively more frequently associated with an agenesis of the permanent lateral incisor (P = .019). Maxillary growth impairment was most severe in Class 3, with a mean sella-nasion-A point angle at 71.9° ± 4.6° (P < .001). Conclusions : Using the cleft width, arch form, and shape of the nasal septum, unilateral cleft lip and palate can be classified into four different classes at birth, which can all give information about permanent lateral incisor agenesis and maxillary growth.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".