Ethical Considerations in Aesthetic Rhinoplasty
Bibliographic record
Abstract
Although the practice of medicine is built on a foundation of ethics, science, and common sense, the increasing complexity of medical interventions, social interactions, and societal norms of behavior challenges the ethical practice of aesthetic surgeons. We report a survey of the opinions, practices, and attitudes of experienced and novice facial plastic surgeons. The survey consisted of 15 clinical vignettes addressing ethical quandaries in aesthetic rhinoplasty. The vignettes are based on the experience and observations of the senior author (P.A.A.) over nearly 30 years of practice and teaching. Fellowship directors and facial plastic surgery fellows of the American Academy of Facial Plastic and Reconstructive Surgery were surveyed anonymously. Five of the 15 vignettes demonstrated significant differences between the responses of the fellowship directors and the fellows. No single vignette had a unanimous consensus in either group. Aesthetic rhinoplasty surgeons encounter ethical issues that should be reflected on by both experienced and inexperienced facial plastic surgeons, preferably before being faced with them in practice. We present a practical approach to ethical issues in clinical practice. Our survey can also be used as a stimulus for further discussion and teaching. Karimi and coauthors present a practical approach to ethical issues in clinical practice of facial plastic surgery. Their survey can also be used as a stimulus for further discussion and teaching.
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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 | 0.042 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| 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 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".