Media portrayal of herbal remedies versus pharmaceutical clinical trials: impacts on decision.
Bibliographic record
Abstract
The use of Complementary and Alternative Medicines (CAM) in Europe and North America is increasing significantly with a concomitant growth in business interest. Users are educated and self-empowered and rely on information sources beyond mainstream medical practitioners. Not surprisingly, media coverage, much of dubious quality, has increased to meet demand for information. Here we present data from a study that explores how knowledge is translated in the socioeconomic-political context of CAM as compared to conventional pharmaceuticals. Specifically, we are interested in the nature of the information provided by clinical trials and the media and how this might impact decision-making regarding the use of CAM versus conventional pharmaceuticals and the reporting of conflicts of interest and industry funding of research. Our results suggest that, in the media, there were significant errors of omission in describing clinical trial quality and a serious under-reporting of risks of herbal remedies. Consumers, who often self-administer CAM are not being provided with information sufficient to make informed choices about treatment alternatives. The next step in the research is to determine whether these reporting dynamics in describing CAM clinical trials differ from those of reporting on pharmaceutical clinical trials.
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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 | 0.026 | 0.185 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".