Assessing the Nasal Midline in Rhinoplasty: How Good Are We?
Bibliographic record
Abstract
BACKGROUND: The challenge of assessing nasal alignment and asymmetry can contribute to high revision rates in rhinoplasty. Comparing to a validated computer algorithm for nasal alignment, the accuracy with which plastic surgeons can assess deviation of the nasal midline from the facial midline was measured. METHODS: Using 20 faces from the Binghamton University 3-dimensional face database, deviation was evaluated from facial midline of the middorsal line for the upper, middle, and lower thirds of the nose. Surgeons were asked to assess extent of deviation from facial midline for each third of the nose using a linear analog scale. Spearman correlations were performed comparing the surgeons' results to the algorithm measurements. Eleven residents and 9 consultant surgeons were tested. RESULTS: Surgeons' assessment of deviation correlated poorly with the algorithm in the upper third ( r =0.32, P <0.0001) and moderately in the middle third ( r =0.49, P <0.0001) and lower third ( r =0.41, P <0.0001) of the nose. No difference in accuracy was found between trainee and consultant surgeons ( P =0.51), and greater experience (>10 y performing nasal surgery) did not significantly affect performance ( P =0.15). The effect of fatigue on the accuracy of assessment was found to be significant ( P =0.0009). CONCLUSIONS: Surgeons have difficulty in visually assessing the 3-dimensional nasal midline irrespective of experience, and surgeon fatigue was found to be adversely affect the accuracy of assessments.
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".