Canadian Society of Plastic Surgeons Société Canadienne des Chirurgiens Plasticiens: Abstracts presented at the 57th Annual Meeting/57e Réunion annuelle June 25–28 juin, Whistler, British Columbia
Bibliographic record
Abstract
A rational treatment plan for correction of the cleft lip nasal deformity (CLND) requires an understanding of the normal anatomy of the nose, the anatomy of the cleft lip nasal deformity, and an appreciation of the mechanism responsible for the deformity. In 1997 we described a model to describe the mechanism of the CLND (Fisher and Mann, PRS May 1997). The model supports the original conclusions of Huffman and Lierle; the CLND is due to structures of the nose being held in abnormal position, rather than an intrinsic defect in the developing nose. The nasal deformity cannot be completely corrected at the time of primary chelioplasty because the asymmetry of the underlying skeleton is not corrected. An approach to management of the CLND is outlined which proposes gradual reversal of the nasal deformity (soft tissue, cartilage and bone) in a stepwise fashion at each available opportunity. The approach stresses the avoidance of additional iatrogenic secondary deformities. For complete clefts the protocol includes: 1) presurgical orthodontia, 2) limited primary rhinoplasty at lip repair, 3) modified Potter rhinoplasty after age six years, 4) alar base and lateral piriform augmentation at alveolar bone grafting at age nine to twelve years, and 5) definitive septorhinoplasty in adolescence. Clinical examples of each stage are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.265 | 0.053 |
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".