Bibliographic record
Abstract
In June, the U.S. Food and Drug Administration announced that it had sent warning letters to 25 companies that peddle unapproved cancer treatments on the Internet. The companies, identified through consumer complaints and a daylong Web search by employees of the FDA, the Federal Trade Commission, and Canadian government agencies, were given 15 days to address the violations outlined in the letters. The companies’ products—including shark cartilage; the botanicals bloodroot and Cat's Claw; ellagic acid, an antioxidant found in pomegranates; and an herbal tea called Essiac—are legal to sell in the United States, said Gary Coody, the FDA's national health fraud coordinator. The problem lies in the claims that the companies make about their products’ intended uses and benefits. Many of the products cited in the warning letters are marketed as “dietary supplements” and are regulated under the 1994 Dietary Supplement Health and Education Act. Supplements are exempt from the rigorous testing and approval procedures that the FDA requires of new drugs, but marketers are prohibited from claiming that their wares prevent, treat, or cure any disease.
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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.048 | 0.015 |
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".