The Impact of COVID-19 on Patient Interest in Facial Plastic Surgery
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has led to an unforeseen surge in demand for facial plastic surgery (FPS). The objective of this study was to survey patients who pursued cosmetic FPS during COVID-19 to better understand how changes in lifestyle, digital media usage, and their facial self-image influenced their decision to pursue surgery. METHODS: A web-based survey was sent to 150 patients who had undergone FPS at an outpatient clinic between May 1 and July 30, 2020. Questions included changes in patients' lifestyle habits, use of video conferencing and social media, Likert scale ratings of motivational factors to pursue FPS, and changes in perception of their own facial aesthetics during COVID-19. RESULTS: The survey response rate was 41%. Overall increases in video conferencing for social (79% of respondents) and occupational (73%) purposes, and social media usage (82%) were noted. The most commonly cited motivating factors to pursue FPS during COVID-19 were having ample privacy from family, friends, and co-workers (77%) and not requiring extended leave of absence from work (69%) during the postoperative recovery period. Patients were more aware of their nose than any other facial feature during COVID-19 compared to prior. CONCLUSIONS: The popularity of FPS during COVID-19 can be partially attributed to increased usage of video conferencing and social media, digital applications which often accentuate personal and idealized facial aesthetics. As surgeons adjust to increased demand for FPS, a better understanding of patient perspectives and motivations can help optimize doctor-patient relations and the delivery of care.
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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 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".