Reply: Ethics in Plastic Surgery: Applying the Four Common Principles to Practice
Bibliographic record
Abstract
Sir: We would like to thank the authors for their response to “Ethics in Plastic Surgery: Applying the Four Common Principles to Practice.”1 We acknowledge that their letter raises important cultural and religious contexts that must be taken into consideration when applying ethical principles in the clinical setting. As argued by Ward2 and reiterated in our work, we are of the opinion that there is currently a paucity of ethics-based articles within the surgical literature, particularly pertaining to plastic surgery. A systematic review by Chung et al.3 identified that only 0.1 percent of an estimated 100,000 plastic and reconstructive surgery–oriented publications focused on topics pertaining to biomedical ethics, which we felt represented “a relatively small proportion of plastic surgery literature.” As such, it is only through bioethical publications and subsequent discussions such as this that the perceived deficiency can be addressed. Although our article attempts to provide all plastic surgeons who may be unfamiliar with the topic of biomedical ethics a basic framework with which to address ethical dilemmas, we do acknowledge within the publication that “[a]lthough these principles may assist health care practitioners to identify the ethical implications of the options presented to them, they only serve as a foundation for decision making. Other factors, such as patient autonomy, aesthetic considerations, cultural fit, and religious factors should also be considered.”1,4 Therefore, their addition of non-Western principles such as the Islamic and Chinese perspective is not only compelling but is particularly relevant to this narratisve. Moreover, it would have been interesting to see whether Hampton and Rahman would have recommended alternative choices to the ones that were recommended in the provided clinical scenarios. Overall, although there will be agreement about generalizable (if not universal) values, how they are prioritized will depend on the patients, their needs, and the clinical matters. In the end, a conscientious practitioner will need to take all of these under consideration and engage with the patient and context openly. DISCLOSURE The authors have no financial interests to disclose. Lucas Gallo, M.D.Charmaine Baxter, M.D.Jessica Murphy, M.Sc.Department of SurgeryDivision of Plastic Surgery Schwartz, Ph.D.Department of Philosophy Achilleas Thoma, M.D., M.Sc.Department of SurgeryDivision of Plastic SurgeryDepartment of Health Research Evidence and ImpactSurgical Outcomes Research CentreMcMaster UniversityHamilton, Ontario, Canada
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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 | 0.009 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.044 | 0.067 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".