Assessing the quality of complementary, alternative, and integrative medicine website information for cancer: A cross-sectional survey and analysis
Bibliographic record
Abstract
Abstract Patients with cancer frequently utilize complementary and alternative medicine (CAM); prior to this, many seek information about these therapies online. Little is known about the quality of this web-based consumer health information (CHI). The present study aimed to address this paucity of research by evaluating the quality of websites containing information on CAM treatments for cancer. Six unique search terms were entered into Google across four English-speaking countries. The first 20 results of each search were assessed and included if they contained CAM consumer health information for the treatment and/or management of cancer. Eligible websites were assessed using the 16-item DISCERN instrument, designed to evaluate information quality. Of the 480 identified websites, 387 were duplicates, and 53 fit the eligibility criteria and were assessed using the DISCERN instrument. Mean summed DISCERN scores across all websites was 53.08 (SD = 12.75), and mean scores of the overall quality of each website was 3.22 (SD = 0.80). Website CHI quality on CAM therapies for cancer were generally acceptable. Several website quality issues were identified, including a lack of transparency surrounding references, areas of uncertainty, consequences of foregoing treatment, and treatment impacts on quality of life.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".