Design Features and Simple Methods of Incorporating Nasal Stents in Presurgical Nasoalveolar Molding Appliances
Bibliographic record
Abstract
Presurgical nasoalveolar molding (NAM) in the orofacial orthopedic treatment of unilateral clefts of the lip and palate aims to align and approximate the maxillary hemialveolar segments and simultaneously support and mold the deformed nasal cartilages, correct and center nasal tip projection, and lengthen the deficient cleft-side columella in early infancy, before the primary reparative lip surgery. A number of techniques of achieving these objectives have been described in the literature and are increasingly being practiced by cleft care teams around the world. However, a detailed description of the nasal stent is lacking in the literature and needs to be elucidated to facilitate greater usage of presurgical NAM in contemporary practice. This report fills this void by providing an analytical description of the different parts of the nasal stent; clarifies their desirable design features, anatomic correlations, and clinical importance; and illustrates in a step-by-step manner simple direct and indirect methods of incorporating a nasal stent, improvised by the author in his practice, that can be used with any of the contemporary NAM appliances and techniques. From the simple methods described, clinicians will be enabled to select one that may be most easily adaptable to their preferred appliance and clinical setting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".