Quality of web‐based medical information on stable COPD: comparison of non‐commercial and commercial websites
Bibliographic record
Abstract
The Internet provides an easy and accessible way to deliver medical information about the management of various diseases, both to practitioners and to their patients. As there is no control over who posts information on the Web, there is a risk that the interests of the web producer may bias the quality of information. The quality of medical information on the management of chronic obstructive pulmonary disease (COPD) on the Internet was evaluated, comparing non-commercial and commercial websites. An internet search was conducted to locate relevant websites using a metasearch engine. The quality of websites was scored on a scale of 0-10, based on three items about the credibility of the site and seven items about the accuracy of the information provided by the site. Quality differences between commercial and non-commercial websites were explored. The search revealed 23 relevant websites (12 noncommercial and 11 commercial). The overall quality of non-commercial websites was better than that of commercial websites (median score 7 vs. 4, P = 0.01). Compared to commercial sites, non-commercial websites more often provided information about cessation of smoking (100% vs. 64%, P = 0.03), preventative influenza vaccinations (42% vs. 9%, P = 0.07) and use of long-term oxygen therapy (92% vs. 45%, P = 0.02). Among websites providing information on COPD, commercial sites were much more likely to be of poorer quality compared to sites of non-commercial organizations. In particular, commercial sites do not provide information about simple preventative treatments. There is a need to be vigilant about the quality of health information about COPD on the Internet.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".