Is Linear Advancement Related to Relapse in Unilateral Cleft Lip and Palate Orthognathic Surgery?
Bibliographic record
Abstract
OBJECTIVE: To investigate the stability of major versus minor Le Fort I maxillary advancements in unilateral cleft lip and palate (UCLP) patients. DESIGN: A retrospective longitudinal study was undertaken on 30 nonsyndromic UCLP patients treated with the same protocol at The Hospital for Sick Children, Toronto, Canada. Patients were grouped into major and minor movement groups based on planned surgical advancement. Standard lateral cephalometric radiographs were taken preoperatively (T1), immediately postoperatively (T2), and at least 1 year postoperatively (T3). Skeletal and dental variables were measured using cephalometric analysis. Stability was compared between groups using repeated-measures analysis of variance. Linear regression analysis was used to assess the relationship between advancement and relapse for the entire study population. RESULTS: A mean maxillary advancement of 9.8 mm and 4.9 mm was seen for the major (n = 10) and minor (n = 20) movement groups, respectively. The mean skeletal horizontal relapse was 1.8 mm (18%) for the major advancement group and 1.5 mm (31%) for the minor advancement group. There was no significant difference in skeletal horizontal relapse between the groups (P > .05). The correlation coefficient (r) between linear horizontal advancement and relapse was calculated to be .31 (P > .05). Dental horizontal relapse was not significant for either the major or minor groups, and no significant difference was found between the groups (P > .05). CONCLUSION: Skeletal and dental relapse was found to be unrelated to the amount of maxillary linear advancement using conventional Le Fort I osteotomies in UCLP.
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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 | 0.001 | 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) | 0.002 | 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".