Web scraping of user-simulated online nutrition information for people with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: People diagnosed with multiple sclerosis (MS) often seek to modify their diet guided by online advice, however this advice may not align with national dietary guidelines. The aim of this study was to simulate an online search for dietary advice conducted by a person with MS and evaluate the content. It was hypothesised that a variety of eating patterns are promoted for MS online and these dietary approaches can be contradictory. METHODS: An online search was simulated using Google Trends-informed search terms and Google and Bing search engines. URLs were extracted using R. Nutrition data were extracted including recommendations for diets, foods, supplements, and health professional consultation. Statistical analyses were conducted using R. RESULTS: 73 URLs from 49 websites were extracted, with only 14 results common to both search engines. Dietary recommendations included overall eating patterns (58 webpages, 79%), individual foods (55 webpages, 75%), and supplements (33 webpages, 45%). The most promoted eating pattern for MS was a balanced diet (33 recommendations, 48%), more likely by nonprofit organisations and health information websites (14 and 17 recommendations, 100% and 89%); lifestyle program websites were more likely to recommend restrictive diets (19 recommendations, 100%) (p<0.001). 52% pages advised consulting a health professional, most often a doctor or dietitian. CONCLUSION: A balanced diet is the most recommended eating pattern for MS online, though advice promoting restrictive diets persists.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".