Accuracy and Reliability of Internet Resources for Information on Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
RATIONALE: Patients commonly use the Internet as a resource for health information; however, no studies have evaluated the online information about idiopathic pulmonary fibrosis (IPF). OBJECTIVES: We sought to determine the readability, content (compared with established guidelines), bias, and quality of online IPF resources. METHODS: We analyzed the first 200 hits for "idiopathic pulmonary fibrosis" in Google, Yahoo, and Bing. Each website was evaluated for content related to IPF features and treatments that are discussed in clinical guidelines. Website quality was assessed using the validated DISCERN instrument. MEASUREMENTS AND MAIN RESULTS: Eligibility criteria were met in 181 websites. The median reading grade level was 12. More content was provided in scientific resources (academic institutions or governmental organizations) and foundation/advocacy organization sites than in personal commentary (blog) sites; however, most sites provided incomplete and/or inaccurate information. Nonindicated and/or harmful pharmacotherapies for IPF were described as potential IPF treatments in 48% of websites and were most often recommended in foundation/advocacy organization websites. Azathioprine and corticosteroids were discussed as potential chronic treatments of IPF in 13.3 and 30.6% of the 98 websites that had been updated after publication of data demonstrating harm from these medications. Website quality (DISCERN score) was poor in all site types but was worse in news/media reports and personal commentary (blog) sites than in sites from scientific and foundation/advocacy organizations. CONCLUSIONS: Patient-directed online information on IPF is frequently incomplete, inaccurate, and outdated. There is no reliable method for patients to identify sites that provide appropriate information on IPF.
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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 | 0.014 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".