The Patient Voice in Aesthetic Medicine: Findings From a Global Survey of Cosmetic Neurotoxin Patients
Bibliographic record
Abstract
Background: Patient satisfaction following cosmetic neurotoxin treatment has been widely studied, but little is known about how this affects downstream behavior. Objectives: To investigate the actions taken by patients following cosmetic neurotoxin injection, with regard to their most and least positive treatment experiences. Methods: This was an online, self-administered survey conducted among adults residing in Brazil, Canada, United Kingdom, and United States. Eligible participants had received 4 or more previous neurotoxin treatments to temporarily improve the appearance of upper facial lines, at least one of which was in the past 12 months. Results: A total of 1612 respondents completed the questionnaire (61% female; mean age: 38.1 ± 9.6 years). After their most positive experience, 81% said they engaged in actions directed toward their healthcare professional (HCP) (eg, scheduled another treatment or posted a review on the HCP's website), and 81% took actions directed at others (eg, talked to friends and family or posted a review online). After their least positive experience, 58% engaged in actions directed toward their HCP (with 22% expressing dissatisfaction directly), and 73% took actions to inform other people; many said they discouraged others from using their HCP (52%) or from seeking cosmetic neurotoxin injections altogether (27%). Conclusions: Respondents were less likely to inform their HCP of their satisfaction level after their least positive experience of cosmetic neurotoxin treatment compared with the most positive. Thus, practitioners may often be unaware of dissatisfied individuals. Patient-centered care and consistent proactive follow-up are essential to understanding patient perspectives on outcomes. Level of Evidence: 5 (Therapeutic).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".