Abstract: Plastic Surgery-Related Hashtag Utilization on Instagram and Implications for Education and Marketing
Bibliographic record
Abstract
INTRODUCTION: Recent data suggests 42 percent of surgeons report their patients are seeking aesthetic surgery to improve their appearance on Instagram and other social media.1 Despite the rising influence of Instagram in plastic surgery, few academic publications address Instagram, let alone evaluate its utilization in plastic surgery. We thus set out to systematically answer the following two questions: 1) what plastic surgery-related content is being posted to Instagram, and 2) who is posting this content? METHODS: Twenty-one Instagram plastic surgery related hashtags were queried. Content analysis was used to qualitatively evaluate each of the nine “top” posts associated with each hashtag (189 posts). Duplicate posts and those not relevant to plastic surgery were excluded. RESULTS: A total of 1,789,270 posts utilized the twenty-one hashtags sampled in this study. Of the top 189 posts for these 21 queried hashtags, 163 posts met inclusion criteria. American Board of Plastic Surgery (ABPS) and Royal College of Physicians and Surgeons of Canada (RCPSC) board certified plastic surgeons accounted for only 17.8% of top posts (29 posts), whereas those not board certified by ABPS or RCPSC accounted for 26.4% (43 posts). Excluding foreign surgeons, otolaryngologists made up the largest group of non-ABPS or RCPSC board certified surgeons, with 7.4% of top posts (12 posts). Also included in this cohort were dermatologists (9 posts), general surgeons (6 posts), gynecologists (4 posts), family medicine physicians (2 posts), and an emergency medicine physician (1 post). All of these non-plastic surgery trained physicians marketed themselves as “cosmetic surgeons”. Nine of these top posts (5.5%) were by non-physicians. This included dentists (4 posts), spas with no associated physician (4 posts), and a hair salon (one post). The majority of these posts were for self-promotional (94 posts, 67.1%) as opposed to educational (46 posts, 32.9%) purposes. Board certified plastic surgeons were significantly more likely to post educational content to Instagram as compared to non-plastic surgeons (62.1% vs. 38.1%, p = .02). CONCLUSION: ASPS board eligible and board-certified plastic surgeons are underrepresented amongst physicians posting top plastic surgery-related content to Instagram. Given that the increasing number of non-plastic surgeons performing cosmetic procedures may come at the expense of patient safety and outcomes,2–4 our findings as mentioned here present a possible cause for concern. Reference Citations: 1. American Academy of Facial Plastic and Reconstructive Surgery Annual Survey Statistics. 2017 Jan. Available at: http://www.aafprs.org/media/stats_polls/m_stats.html. 2. Mioton LM, Buck DW II, Gart MS, Hanwright PJ, Wang E, Kim JY. “A Multivariate regression analysis of panniculectomy outcomes: Does plastic surgery training matter?” Plast Reconstr Surg. 2013; 131: 604e-612e. 3. O’Donnell J. “Lack of training can be deadly in cosmetic surgery.” USA Today. September 15, 2011. 4. Shah A, Patel A, Smetona J, Rohrich RJ, “Public Perception of Cosmetic Surgeons versus Plastic Surgeons: Increasing Transparency to Educate Patients.” Plast Reconstr Surg. 2017 Feb; 129(2):544e-557e.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".