Online nutrition information for cancer survivors
Bibliographic record
Abstract
BACKGROUND: This research aims to systematically review a comprehensive sample of websites (English-language) that provide information on nutrition after cancer treatment. METHODS: In consultation with cancer survivors and experts, we developed search strings for an internet search (incognito mode in Google Chrome) to be completed in six English-speaking countries (Ireland, the United Kingdom, Australia, New Zealand, Canada and the United States); the first 10 results were chosen for each search). Websites/web pages were included if the links related to sites/content that provided information on health post-treatment in English and aimed at adults (aged above 18 years). Several tools were applied to test the quality, readability and usability of the websites/weblinks. RESULTS: Initially, 720 links were found, with 159 eligible for inclusion. Those eligible for review were charity/support/non-governmental organisation weblinks (49.1%) that originated in the United States (42.8%) and did not specify a particular cancer type (65.4%). One-third (n = 59, 37.1%) of these links contained nutrition guidance; however, these lacked practical implementation strategies. Most of the websites/web pages were not Health On the Net certified and lacked overall quality, with a mean International Patient Decision Aids Standards score of 20.4/40 and a Journal of the American Medical Association score of 1/4. Readability failed to meet ideal levels. Only 32.5% (n = 13) of the web pages/weblinks met the benchmark for usability. CONCLUSION: Cancer survivors seeking nutrition information online may encounter difficulty locating advice, and where they do, it is unlikely to contain guidance on implementation into day-to-day life. This is concerning, given the important role nutrition can play in cancer survivorship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".