Computer-Aided Design and Computer-Aided Manufacturing Technology for Conducting Nasoalveolar Molding for Infants With Cleft Lip and Palate: A Scoping Review
Bibliographic record
Abstract
ObjectiveTo identify, describe, and characterize computer-aided design and computer-aided manufacturing (CAD/CAM) methods for nasoalveolar molding (NAM) based on a structured search of scientific literature.DesignScoping review was conducted following the PRISMA-ScR guidelines. Searches were done in MEDLINE, Embase, Web of Science, Cochrane Library, and Scopus. Screening and data extraction were performed.PatientsInfants with unrepaired, nonsyndromic, complete unilateral cleft lip and palate (UCLP), or bilateral cleft lip and palate (BCLP).InterventionCAD/CAM NAM.Main Outcome MeasuresOutcome measures were the digitization, virtual modeling, and manufacturing protocols.ResultsThirteen articles were included. CAD/CAM NAM involved digitizing the maxilla, designing step-by-step (stepwise) alveolar movements or expansion of plates, then manufacturing plates manually or through 3D printing. Four methods were characterized based on the virtual modeling and manufacturing techniques employed: stepwise alveolar molding and manually fabricated plates (SM_MP); stepwise alveolar molding and 3D-printed plates (SM_3P); stepwise plate expansion and 3D-printed plates (SPE_3P); and semi-automated plate expansion and 3D-printed plates (SAPE_3P). The SM_MP method was the most common, followed by the SM_3P, SPE_3P, and SAPE_3P methods. All methods were applied to treat infants with UCLP, whereas only the SM_MP and SM_3P methods were used for infants with BCLP.ConclusionsThis scoping review provides an overview of 4 CAD/CAM methods for NAM. The SM_MP and SM_3P methods simulate alveolar molding; however, the SM_3P method exhibits more advanced design and manufacturing of plates. The SPE_3P and SAPE_3P methods design consecutively enlarged plates, with the latter employing a semi-automated protocol.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".