Bilateral Cleft Lip and Palate: Improved Maxillary and Dental Development
Bibliographic record
Abstract
BACKGROUND: Maxillary growth deficiency is still a problem in children with repaired cleft lip-cleft palate, and little progress has been made on research for its prevention. An increase in the early diagnosis of maxillary growth deficiency prompted this study to obtain an early indication of the efficacy of a new protocol for better dental and maxillary development. METHODS: A new method for repair of the hard palate in infants with bilateral cleft lip-cleft palate that made innovative use of mucosa of the nasal septum and involved less extensive surgery was coupled with the repair of the lip at 18 months instead of at 8 months. Twelve consecutive cases were compared with 12 cases treated by the previous method. Records of dental occlusion and lateral head radiographs at 5 years were obtained prospectively for the new treatment group and retrospectively for the previous group. RESULTS: In the previous group, there was a high incidence of dental crossbite. Incisor crossbite was present in 10 of the 12 (83 percent), and all 12 had one or both cuspids in crossbite. In the new treatment group, the incisor crossbite was reduced to four of 12 cases (33 percent), with cuspid crossbite at 50 percent. The new treatment group also showed greater values for cephalometric measures in maxillary length, maxillary prominence, and the ANB angle. CONCLUSION: At the age of 5 years, a definite improvement in dental and maxillary development was evident in the new treatment group.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".