Is Presurgical Nasoalveolar Molding Therapy More Effective in Unilateral or Bilateral Cleft Lip–Cleft Palate Patients?
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to compare the efficacy of presurgical nasoalveolar molding in treating unilateral versus bilateral cleft lip–cleft palate patients. METHODS: A blinded, retrospective study was conducted with 16 unilateral and 13 bilateral cleft lip–cleft palate patients. Pretreatment and posttreatment facial and intraoral impressions were used to compare soft- and hard-tissue changes. RESULTS: Nasoalveolar molding therapy improves nasal angle in unilateral (p = 0.010) and bilateral cleft lip–cleft palate (p = 0.001) patients, and improves nostril width in unilateral (p = 0.005) and bilateral cleft lip–cleft palate (p = 0.028) patients. Treatment significantly improves nostril breadth only in unilateral cleft lip–cleft palate patients (p = 0.005). Compared with bilateral cleft lip–cleft palate patients, unilaterally affected patients were more asymmetric before and after nasoalveolar molding therapy. Nasoalveolar molding more effectively increases columellar height (p = 0.002) and columellar width (p = 0.002) in the bilateral cleft lip–cleft palate group. Although starting bialar widths did not significantly differ between the two groups, nasoalveolar molding significantly decreased bialar width only in unilateral cleft lip–cleft palate patients (p = 0.032). When the intersegment alveolar cleft distances of the bilateral cleft lip–cleft palate patients were summed, an improvement similar to that in unilateral cleft lip–cleft palate patients was observed. Furthermore, nasoalveolar molding appeared to prevent alveolar width widening as patients continued to grow. CONCLUSIONS: There are differences in efficacy between unilateral and bilateral cleft lip–cleft palate patients undergoing nasoalveolar molding. Understanding these differences may help physicians and dentists better shape expectations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".