Quality of online information on pulmonary arterial hypertension
Bibliographic record
Abstract
Background: Patients with pulmonary arterial hypertension (PAH) frequently search the internet for information about their disease. The quality of PAH websites is unknown. Aims: To assess the readability, transparency/reliability and quality of PAH websites. Methods: We searched Google, Yahoo, and Bing for “pulmonary arterial hypertension” and screened the first 200 sites from each search engine. We evaluated website quality using the validated DISCERN tool (best score is 80) and JAMA Benchmark Criteria (best score is a 4). Results: 122 eligible sites were evaluated (31% from foundations, 25% scientific organizations, 19% industry, 17% personal commentary, 8% news media sites). Of 86 sites reporting the date of the last update, median time since last update was 14 months (range 0.1-121). Mean Flesch-Kincaid reading ease level was 39±15 and reading grade was 12.1±2.8, indicating high-school or college level reading difficulty. Only 23% had HonCode certification for ethical presentation of healthcare information. Mean JAMA Benchmark score was 1.3±1.2 and mean DISCERN score was 29.4±9.9, indicating generally poor transparency/reliability and quality of information, respectively. The top 3 websites by DISCERN score were from PHAUK.org (65), nhsinform.scot (59), and mayoclinic.org (54). PAH information gaps were exercise/rehabilitation (mentioned in 28 sites), finances (8 sites), and palliative care (1 site). Personal commentary sites often contained inaccurate or misleading information such as treating with Co-Enzyme Q 10, L-Carnitine, avoiding milk products, and drinking water to “flush your system”. Conclusions: There is a relative lack of easily readable, comprehensive and transparent online patient information on PAH.
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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 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".