Why this work is in the frame
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Bibliographic record
Abstract
IMPORTANCE: The prediction of nasal tip position in terms of projection, rotation, and length is a major challenge in rhinoplasty. Studies using preoperative and postoperative photographs lack accuracy owing to variable position, and computer-simulated models lack clinical applicability. OBJECTIVES: (1) To describe an accurate and reproducible technique to study the effect of surgical manipulations on the nasal tip; and (2) to describe the effect on the nasal tip cartilages of the lateral crural steal (LCS). DESIGN, SETTING, AND PARTICIPANTS: Cadaveric study in a tertiary hospital center using 10 cadaveric specimens. INTERVENTIONS: Heads were placed in a Mayfield head holder, and a 12.2-megapixel camera was fixed on a tripod in a perfectly still position and focused on the surgical field during all surgical manipulations. An external rhinoplasty approach was performed for all specimens, and a 4-mm LCS was achieved. MAIN OUTCOMES AND MEASURES: Measures include tip projection, tip rotation, and nasal length using preoperative and postoperative photographs. RESULTS: Our method was successfully performed on all specimens: LCS resulted in a significant mean increase in projection using the Goode ratio (mean, 0.05; P = .005) and rotation (mean, 13.2°; P = .005). However, absolute tip projection variation was inconsistent, ranging from -1.0 mm to 0.6 mm. Nasal length was significantly shortened in all cases (mean, 1.3 mm, P = .005). CONCLUSIONS AND RELEVANCE: We describe the first technique for precise anatomical study of tip position in rhinoplasty on cadaveric specimens. This technique was successfully applied to 10 consecutive nasal tips. We have shown a significant increase in projection using the Goode ratio and rotation with LCS. However, the effect on absolute projection is inconsistent. LEVEL OF EVIDENCE: NA.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it