Aortic valve surgery: how reliable are health information websites?
Bibliographic record
Abstract
BACKGROUND: Aortic valve replacement is one of the most common cardiac operations currently performed. Patients increasingly use the internet for information about their diagnosis and it would therefore be important to know how reliable this is. AIM: To determine the reliability of internet information on aortic valve replacement surgery. DESIGN & SETTING: This was a web-based project scoring sites that might be accessed by a patient. METHOD: The first 50 websites found on each of the four most popular search engines in the UK were viewed, as well as the first 50 videos found on the most popular video-host website. Eligible websites were assessed according to seven positive criteria and three negative criteria, giving a possible range of scores from -6 to 14. RESULTS: There were 79 sites and the median score was 5 (range -1 to 14). There were statistically significant differences between organisation/educational sites with score 7 (2 to 14), hospital sites with score 2 (-1 to 10), commercial sites with score 2.5 (0 to 9) and videos with score 5 (2 to 11). The highest scores went to three NHS sites (score 13 or 14), .gov sites (median score 8.5) and Health On the Net Foundation (HON) accredited sites (median score 7). CONCLUSION: Information on the internet about aortic valve replacement is variable but NHS sites provide the most reliable information.
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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 | 0.010 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".