Informed Consent in Facial Plastic Surgery
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of oral communication about the risks of facial cosmetic procedures compared with oral and written communication. DESIGN: A prospective randomized study conducted in an ambulatory surgery center. One hundred twenty consecutive patients were included; they presented for consultation for rhinoplasty, rhytidectomy, or laser resurfacing. Patients were randomly assigned to 1 of 2 groups: (1) those receiving oral discussion of the risks of the procedure and (2) those receiving oral and written communication about the risks. Two weeks after the initial consultation, patients were surveyed for recall of the risks. RESULTS: The group that received a pamphlet had a better risk recall than the group that did not (2.5 vs1.5 of 5 risks; P<.001). The recall rate in the following groups that received a pamphlet was also better: (1) university-educated patients (P =.02), (2) patients who underwent rhinoplasty (P<.001), (3) patients who underwent laser resurfacing (P =.02), and (4) female patients (P<.001). CONCLUSIONS: Written disclosure of the risks of cosmetic procedures enables patients to retain and understand more clearly those potential risks. They are, therefore, able to give an informed consent to the proposed procedure. This study also identifies patient groups who may require more intensive presurgical teaching. The medicolegal implications are apparent.
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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 | 0.024 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".