What Are Your Patients Reading Online About Soft-tissue Fillers? An Analysis of Internet Information
Bibliographic record
Abstract
BACKGROUND: Soft-tissue fillers are increasingly being used for noninvasive facial rejuvenation. They generally offer minimal downtime and reliable results. However, significant complications are reported and patients need to be aware of these as part of informed consent. The Internet serves as a vital resource to inform patients of the risks and benefits of this procedure. METHODS: Three independent reviewers performed a structured analysis of 65 Websites providing information on soft-tissue fillers. Validated instruments were used to analyze each site across multiple domains, including readability, accessibility, reliability, usability, quality, and accuracy. Associations between the endpoints and Website characteristics were assessed using linear regression and proportional odds modeling. RESULTS: The majority of Websites were physician private practice sites (36.9%) and authored by board-certified plastic surgeons or dermatologists (35.4%) or nonphysicians (27.7%). Sites had a mean Flesch-Kincaid grade level of 11.9 ± 2.6, which is well above the recommended average of 6 to 7 grade level. Physician private practice sites had the lowest scores across all domains with a notable lack of information on complications. Conversely, Websites of professional societies focused in plastic surgery and dermatology, as well as academic centers scored highest overall. CONCLUSIONS: As the use of soft-tissue fillers is rising, patients should be guided toward appropriate sources of information such as Websites sponsored by professional societies. Medical professionals should be aware that patients may be accessing poor information online and strive to improve the overall quality of information available on soft-tissue fillers.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".