ArticlePlastic and reconstructive surgery. Global open2020
Bifurcation of Patient Reviews: An Analysis of Trends in Online Ratings.
Article in Plastic and reconstructive surgery. Global open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 13 citations in OpenAlex.
- RealSelf Assessment of Patient Narratives and Drivers of Dissatisfaction Following Augmentation-Mastopexy: A Quantitative and Qualitative Analysis.Aesthetic plastic surgery · 2026Article
- Does Patient Experience Correlate With Overall Hospital Quality Ratings?Journal of patient experience · 2026Article
- Sentiment analysis in medication adherence: using ruled-based and artificial intelligence-driven algorithms to understand patient medication experiences.International journal of clinical pharmacy · 2025Article
- The Patient Voice in Aesthetic Medicine: Findings From a Global Survey of Cosmetic Neurotoxin Patients.Aesthetic surgery journal. Open forum · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOnline reviews have become increasingly important drivers of healthcare decisions. Data published by the Pew Research Center from 2016 suggest that 84% of adult Americans use online rating sites to search for information about health issues. The authors sought to analyze physician reviews collected from a large online consumer rating site to better understand characteristics that are associated with positive and negative review behavior.
methodsPublished patient reviews from RealSelf were sampled over a 12-year period (June 2006 to August 2018). SQL, Python, and Python SciPy were used for statistical analysis on 156,965 reviews of 10,376 unique physicians. Python VADER was used to quantify consumer sentiment with review text as input.
resultsSurgical procedures tended to be higher rated than nonsurgical treatments. The highest-rated procedures were breast augmentation, rejuvenation of the female genitalia, and facelift. The lowest-rated surgical procedures were buttock augmentation, rhinoplasty, and eyelid surgery. The mean physician rating was 4.6, with 87% of reviews being 5-star and 5% being 1-star. Sentiment analysis revealed positive consumer sentiment in 5-star reviews and negative sentiment in 1-star reviews.
conclusionsThese findings suggest that online reviews of doctors are polarized by extreme ratings. Within the surgical category, significant differences in ratings exist between treatments. Perceived problems with postprocedural care are most associated with negative reviews, whereas satisfaction with a physician's answers to patient questions is most associated with positive reviews. Polarization of physician reviews may suggest selection bias in reviewer participation.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.