Bibliographic record
Abstract
IMPORTANCE: The different nasal osteotomy patterns used to perform rhinoplasty are poorly described in the literature, and there is a continuous debate between surgeons on the ideal sequence and technique to obtain desired results. OBJECTIVES: (1) To evaluate the necessity of a paramedian osteotomy when performing a high-low-high (HLH) osteotomy, (2) to study the fracture pattern of a high-low-low (HLL) osteotomy when combined with a paramedian osteotomy in the presence and in the absence of a transverse osteotomy, and (3) to evaluate the mobility of the central segment (located between the paramedian osteotomies) after digital pressure and the ideal osteotomy to mobilize it if needed. DESIGN AND SETTING: This was a prospective cadaveric study performed in the dissection laboratory in our tertiary referral center. EXPOSURE: Twenty cadavers were divided in 2 groups of 10. Group A had a paramedian osteotomy combined with an HLH osteotomy on 1 side and an HLH osteotomy alone on the other side. Group B had a paramedian combined with a transverse osteotomy followed by HLL osteotomy on 1 side. On the other side, we performed a paramedian combined with an HLL osteotomy. Finally, we evaluated the mobility of the central segment in group B, first with digital manipulation and then with a transverse osteotomy. MAIN OUTCOME AND MEASURE: The 3 authors evaluated individually the different fracture patterns. A result was considered successful when (1) the fracture followed the desired pattern, (2) a continuous line was obtained, and (3) a complete mobilization of the nasal segment was visualized. RESULTS: In group A, the side without a paramedian osteotomy had more unstable and greenstick fractures than the other side (P < .001). In group B, the side with a transverse osteotomy had more reliable and stable fractures than the other side (P < .05). Digital manipulation alone was not enough to mobilize the central segment in 8 of the 10 cadavers studied. CONCLUSIONS AND RELEVANCE: Following this study we make the following suggestions: (1) to perform a paramedian osteotomy when an HLH osteotomy is needed, (2) to perform a transverse osteotomy before an HLL osteotomy when combined with paramedian osteotomy, and (3) to manipulate the central segment with a transverse osteotomy in order to mobilize it in a safe and predictable manner. LEVEL OF EVIDENCE: NA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".