Graft Outcomes With or Without Use of Cortical Bone Graft for Nasal Floor Reconstruction in Unilateral Cleft Lip and Palate
Bibliographic record
Abstract
Objective Evaluate the use of cortical bone grafts (CBGs) for nasal floor reconstruction on alveolar bone graft (ABG) outcomes in unilateral cleft lip and palate (UCLP). Design Retrospective cohort study. Setting North-American cleft centers. Subjects Fifty-three children with nonsyndromic UCLP treated with secondary ABG. Methods Center 1 ( n = 23) utilized CBG for nasal floor reconstruction, while Centers 2 ( n = 10), 3 ( n = 10), and 4 ( n = 10) did not. Occlusal radiographs, taken 6 to 18 months after ABG, were assessed by 6 calibrated raters using the Standardized Way to Assess Grafts (SWAG) Scale. Weighted kappa statistics measured intra and interrater reliabilities. SWAG scores comparisons in the apical, middle, and coronal thirds of the alveolar cleft were conducted between the centers. The Kruskal–Wallis test was used to determine significant differences, with a P -value of <.05 considered statistically significant. Results Intrarater reliability was very good (κ = 0.858), and interrater reliabilities were good (κ = 0.717). Significant differences were initially found in overall SWAG scores across centers ( P = .003), with Center 1 achieving the highest mean overall SWAG score (5.16 ± 0.99) compared to Centers 2, 3, and 4 (4.48 ± 1.37, 4.58 ± 1.44, and 3.72 ± 0.65, respectively). Center 4 exhibited statistically significant lower scores compared to Centers1, 2, and 3, and was excluded from secondary analyses. After exclusion, no significant differences remained among Centers1, 2, and 3 ( P = .135) for overall SWAG scores or apical third SWAG scores (where CBG is inserted). No significant differences were observed in the middle ( P = .055) or coronal ( P = .131) thirds across centers. Conclusions CBG in secondary ABG procedures for CUCLP patients showed outcomes comparable to cancellous-only grafts, with a trend toward improved apical scores in CBG-treated patients.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".