An Updated Review of Plastic Surgery-Related Hashtag Utilization on Instagram: Implications for Education and Marketing
Bibliographic record
Abstract
BACKGROUND: The popularity of social media continues to have a significant impact in the plastic surgery industry. Understanding the influence of such platforms and recognizing trends, specifically on Instagram, can reveal significant implications for education and marketing. OBJECTIVES: This study aims to gather updated information on 3 main questions: (1) what plastic surgery-related content is being posted to Instagram; (2) who is posting this content; and (3) what specific hashtags are they using? METHODS: This study analyzed 22 plastic surgery-related hashtags on Instagram. Content analysis was then used to qualitatively evaluate each of the 9 "top" posts associated with each hashtag (198). Any duplicates or posts not relevant to plastic surgery were excluded. RESULTS: A total of 11,516,969 posts utilized the 22 hashtags sampled. Of the top 198 posts, only 168 met final inclusion criteria (after duplicates and posts irrelevant to plastic surgery were excluded). Plastic surgeons eligible for membership in The Aesthetic Society accounted for only 4.17% of top posts (7 posts), whereas non-eligible physicians accounted for 20.8% (35 posts). Twenty-eight surgeons accounted for the top posts (excluding foreign surgeons); however, only 6 were board certified by either the American Board of Plastic Surgeons or The Royal College of Physicians and Surgeons of Canada. CONCLUSIONS: The Aesthetic Society eligible board-certified plastic surgeons are a minority amongst physicians posting top plastic surgery-related content on Instagram.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".