Surgical Correction of Secondary Bilateral Cleft lip and Nasal Deformities Using the Delaire-Precious Technique
Bibliographic record
Abstract
ObjectiveThis study aimed to determine the efficacy of the surgical correction of secondary bilateral cleft lip and nasal deformities using the Delaire-Precious technique for Asian patients with bilateral cleft lip +/- palate (BCL+/-P).DesignRetrospective cohort study.SubjectsThirty-six patients with BCL+/-P in Japan, the Philippines, and Vietnam underwent secondary lip correction using the Delaire-Precious technique by a single surgeon.MethodsThe critical concepts of this surgical technique are discussed and clarified. A patient/parent satisfaction survey was carried out to evaluate the usefulness of this technique.ResultsThe Delaire-Precious technique improves Cupid's bow's symmetry and the central tubercule's volume. The scar tissue between lateral and medial philtrum incisions is excised. The orbicularis oris muscle is then reconstructed with a midline suture placed above the periosteum of the premaxilla. Most patients (90.9%) and all parents were at satisfied with the surgical result. The technique was highly satisfactory to patients and parents except at the level of scar correction.ConclusionThe secondary bilateral cleft lip and nasal repair using the Delaire-Precious technique is an adaptable technique that can be applied to various ethnic groups. It was found to be a satisfactory technique for Asian patients with BCL+/-P.
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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) | 0.002 | 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".