Transverse Slicing of the Sixth–Seventh Costal Cartilaginous Junction
Bibliographic record
Abstract
BACKGROUND: Costal cartilage is an important reconstructive tissue for correcting nasal deformities. Warping of costal cartilage, a recognized complication, can lead to significant functional and aesthetic problems. The authors present a technique to prevent warping that involves transverse slicing of the sixth-seventh costal cartilaginous junction, that when sliced perpendicular to the long axis of the rib, provides multiple long, narrow, clinically useful grafts with balanced cross-sections. The aim was to measure differences in cartilage warp between this technique (TJS) and traditional carving techniques. METHODS: Costal cartilage was obtained from human subjects and cut to clinically relevant dimensions using a custom cutting jig. The sixth-seventh costal cartilaginous junction was sliced transversely leaving the outer surface intact. The adjacent sixth rib cartilage was carved concentrically and eccentrically. The samples were incubated and standardized serial photography was performed over time up to 4 weeks. Warp was quantified by measuring nonlinearity of the grafts using least-squares regression and compared between carving techniques. RESULTS: TJS grafts (n = 10) resulted in significantly less warp than both eccentrically (n = 3) and concentrically carved grafts (n = 3) (P < 0.0001). Warp was significantly higher with eccentric carving compared with concentric carving (P < 0.0001). Warp increased significantly with time for both eccentric (P = 0002) and concentric (P = 0.0007) techniques while TJS warp did not (P = 0.56). CONCLUSION: The technique of transverse slicing costal cartilage from the sixth-seventh junction minimizes warp compared with traditional carving methods providing ample grafts of adequate length and versatility for reconstructive requirements.
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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 | 0.000 |
| 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 | 0.001 |
| 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".