Evaluating the quality and readability of online information about hidradenitis suppurativa: a systematic review
Bibliographic record
Abstract
Hidradenitis suppurativa (HS) is a chronic inflammatory skin disorder associated with considerable physical, psychological and social burdens. Despite increasing recognition, diagnostic delays remain common, often prompting patients to seek information online. In this systematic review, we evaluated the quality and readability of HS-related information across artificial intelligence (AI)-generated content, search--engine-derived resources and social media platforms. A comprehensive search of PubMed, Embase, Cochrane Library and Google Scholar identified 17 studies published between 2017 and 2024 that assessed HS-related online content using validated scoring tools and/or physician evaluation. More than 50% of studies rated online HS materials as variable in quality, with 36% rating them as moderate. Readability -assessments revealed that most resources exceeded the recommended sixth-grade to eighth-grade school level, limiting accessibility for patients. Social media platforms, particularly TikTok and YouTube, featured highly engaging but frequently inaccurate or anecdotal content, with physician-generated materials receiving lower engagement than nonmedical resources. These findings highlight the critical need for simplified, -evidence-based online resources to improve health literacy and support informed decision-making by patients with HS. The prevalence of misinformation, particularly regarding alternative treatments and pharmaceutical scepticism, underscores the urgent need to develop enhanced patient education strategies. Future efforts should focus on AI-driven readability improvements, clinician engagement in digital education and collaboration with social media platforms to ensure the availability of accessible, high-quality HS information.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".