Drug Quality, Safety Issues and Threats of Drug Importation
Bibliographic record
Abstract
Until the last decade, the U.S. Food and Drug Administration (FDA) had tight control over drug importation; principally, drug manufacturers were the only entities which could legally import pharmaceuticals.However, in the last ten years, the FDA's tight control has weakened.The number of U.S. residents who personally import prescription medications has increased dramatically.For example, in 2004, over two million U.S. residents purchased over twelve million prescriptions from Canada alone.The number of prescriptions purchased from other countries is unknown, but it is estimated that Americans buy $800 million in drugs from Mexico.Additionally, drug products are entering the United States from across the globe.Drug importation not only affects the economy; importation presents serious drug safety issues for the public.One problem is that some pharmacy Web sites where individuals obtain their prescription drugs are rogue, fraudulent sites, with many selling substandard, counterfeit drug products.Furthermore, many pharmacy Web sites do not require a prescription and thus some sites are shipping a plethora of narcotic and non-narcotic drugs to Americans.Thus, medical supervision by licensed health care practitioners is missing.Some imported products are of poor quality and are counterfeit.The imported drugs have no active ingredients, wrong active ingredients, or are poorly labeled and packaged.However, some imported drug products are FDA-approved and are the same quality as products from U.S. pharmacies.The problem is distinguishing the "good" products from the substandard products.However, documentation of the adverse effects from imported,
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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 | 0.036 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".