Septopalatal Protraction for Correction of Nasal Septal Deformity in Cleft Palate Infants
Bibliographic record
Abstract
OBJECTIVE: It is proposed to test the practicality of septopalatal protraction in the unilateral cleft palate infant for purposes of straightening the nasal septum and thus relieving nasal airflow obstruction and its detrimental sequelae. METHODS: Alternate infants affected with complete unilateral palatal clefts had septopalatal protraction for a period of 6 to 8 weeks (protraction group; n = 4). Septal deviation was measured by a standardized technique that used computed tomography scans. The remaining infants had no protraction and served as controls (nonprotraction group; n = 5). Septal deviation was measured in the nonprotraction group from palatoseptal dental molds. RESULTS: A total of 9 patients were studied. All patients in the nonprotraction group had worsening of nasal septal deviation over a period of 8 weeks compared with the protraction group, which had complete nasal septal straightening. Differences in septal angle deviation between the protraction group and nonprotraction group at the end of the study were statistically significant (P < or = 0.01) as measured by the paired Student t test. CONCLUSIONS: Septopalatal protraction in the newborn appears to provide a means for correcting nasal septal deviation in complete unilateral cleft palate infants. Septopalatal protraction in the newborn is relatively easy and safe.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".