PWE-430 Quality of irritable bowel syndrome information on the internet
Bibliographic record
Abstract
Introduction To measure the search demand for websites with Irritable Bowel Syndrome (IBS) information and to review the quality of the typical search results returned. Method Between January 2009 and December 2014 the Google search engine was used to provide search trend analysis and the top 10 search results of each year. Websites were critically reviewed using the Minervation (LIDA) healthcare website tool. Website accessibility, usability, reliability and overall quality were recorded. Six countries, the USA, UK, Canada, Ireland, Australia, New Zealand and South Africa were studied. Results The mean (s.d.) search demand for online information about IBS has increased significantly from 51.6 (12.7) in 2009 to 65.8 (19.5) in 2014 (r²=0.064 P < 0.001). The USA, UK, Australia, New Zealand and Ireland all showed a significant increase, whereas demand in South Africa actually fell. The vast majority of websites were commercial healthcare information portals (n = 109, 26%) medicinal or product advertisements (n = 88, 21.0%) or healthcare professional advertisements (n = 80, 19.0%). Website quality was variable. Overall the USA and UK had the best online healthcare resources available, whereas most other counties were low quality and dominated by commercial advertisements. Conclusion Online demand for healthcare information about IBS is increasing, however, a significant number of online healthcare resources are unreliable and of poor quality. Islands of high quality information do exist but they are often very academic in nature. Disclosure of interest None Declared.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".