NAM Therapy—Evidence-Based Results
Bibliographic record
Abstract
Many orthodontists working on patients with cleft lip and palate (CLP) have shown great enthusiasm for presurgical infant orthopedics (PSIO) to improve surgical outcomes with minimal intervention. Even though every clinician aims to use the best treatment modality for their patients, PSIO effects can be confounded by surgical type and timing of the primary repair, as is discussed in many studies. In such cases, one should be cautious when evaluating the particular outcomes for patients with CLP since it is difficult to differentiate the sole effect of an individual surgical or orthodontic intervention. As with any treatment methodology, nasoalveolar molding (NAM) has both benefits and limitations. Commonly cited concerns with NAM, and PSIO in general, include increased cost, increased burden of care, and a negative impact on maxillary growth. However, NAM cannot be deemed as having apparent long-term negative or positive effects on skeletal or soft tissue facial growth, based on previous studies. A review of the literature suggests that NAM does not alter skeletal facial growth when compared with the samples that did not receive PSIO. Nevertheless, the published studies on NAM show evidence of benefits to the patient, caregivers, the surgeon, and society. These benefits include documented reduction in severity of the cleft deformity prior to surgery and as a consequence improved surgical outcomes, reduced burden of care on the care givers, reduction in the need for revision surgery, and consequent reduced overall cost of care to the patient and society.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".