Informing, advising, or persuading? An assessment of bone mineral density testing information from consumer health websites
Bibliographic record
Abstract
UNLABELLED: Greater access to web-based information on health-care interventions might result in greater participation by patients in care and self-care decisions, but only improve health outcomes if the indicated actions produce the intended benefits. Unbiased research on benefits and harms of health information can provide a basis for evidence-based patient information systems. OBJECTIVES: To evaluate the quality of the information content on bone-mineral density (BMD) testing posted on consumer health websites (CHWS). METHODS: Five popular engines (Yahoo, MSN, AOL, Lycos, and Go.com) were used to search for patient information on bone densitometry. The fifteen websites that supplied relevant content and were identified by three of the five search engines were selected in order of popularity of the search engine and primacy of placement. Six BMD reports from health technology assessment (HTA) organizations were used as a standard of scientific quality. These were identified from the HTA Database at York University United Kingdom and published between 1996 and 2001. Content was extracted from both document types, and these sets were compared independently by two reviewers. RESULTS: The majority of CHWS identified by popular search engines do not disclose the limited capacity of BMD to discriminate between low-risk individuals and those who will suffer future fractures. CHWS generally present BMD testing as quick, painless, noninvasive, and as being recommended, based on risk factors that are widespread among the general public. BMD testing information is prominently paired on CHWS sites with information on osteoporosis, with an emphasis on "silent disease" and the devastating consequences of advanced disease. Sponsors of CHWS sites are frequently either providers of BMD testing or companion drugs, and consequently in a position of conflict of interest with regard to decisions to undergo BMD testing. HTA organizations have no documented conflict of interest, nor do they invoke emotional arguments. Their approach is to emphasize the effects of testing on populations, on the basis of referenced research findings. CONCLUSIONS: Content analysis demonstrates the omissions and divergence of information on BMD testing available to consumers on the Internet, as compared with HTA reports. The content of HTA reports has undergone rigorous systematic and peer review; therefore, their findings may be useful to consumers. This information is not generally accessible to patients using the most popular Internet search engines. Inaccurate and incomplete information may cause harm by deflecting patients from optimal decisions.
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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 | 0.069 | 0.359 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".