Confusion and Contradiction: Untangling Drug Importation and Counterfeit Drugs
Bibliographic record
Abstract
BACKGROUNDDrug importation first became big news in the United States when busloads of seniors began crossing the border to buy cheaper drugs from Canadian pharmacies.'Media reports soon followed that Americans were buying drugs from Canadian Internet pharmacies.Numerous bills were introduced in the House and Senate, each aimed at creating an importation scheme for prescription drugs. 2 At the same time, articles and books were being written about the problem of counterfeit drugs in America 3 and the fact that many Internet pharmacies claiming to be Canadian were not in fact based in Canada and were not selling Canadian-approved drug products.More broadly, Americans learned that a significant portion of the drugs available through the Internet were counterfeit.These two stories-importation and counterfeit drugs-have now become intertwined and inseparable.There is a growing perception that drugs imported into the United States have a much greater chance of being counterfeit.Underlying this belief appears to be a sort of xenophobic sentiment, a fear of drugs that are anything but homegrown.Yet, an important fact appears to have been forgotten: American drugs--drugs that are approved by the Food and Drug Administration (FDA) for sale in the United States-are manufactured all over the world.41. See IMS, RETAIL PRESCRIPTIONS GROW AT RECORD LEVEL IN 2003 (2004), http://www.imshealthcanada.con/htmen/1_09.htm.Although Food and Drug Administration (FDA) regulations currently prohibit retail pharmaceutical imports from Canada, a discretionary policy of the FDA and U.S. Customs Service allows a significant volume of imports to the United States.Total imports under the Customs Service personal use exemption totaled approximately $600 million in 2003.Id. 2. Safe Import Act of 2005, S. 184, 109th Cong.(2005); Pharmaceutical Market Access Act of 2005, H.R. 328, 109th Cong.(2005); Pharmaceutical Market Access and Drug Safety Act of 2005, S. 334, 109th Cong.(2005).These three bills are currently making their way through Congress and are commonly called the Gregg Bill (S. 184), the Gutknecht Bill (H.R. 328), and the Dorgan-Snowe Bill (S. 334).For more information on the progress of these bills, see Thomas: Legislative Information on the Internet, http://thomas.loc.gov(insert the above-mentioned bill numbers into a search of the 109th Congress). See, e.g., KATHERINE EBAN, DANGEROUS DOSES: How COUNTERFEITERS ARE CONTAMINATING AMERICA'S DRUG SUPPLY (2005).4.
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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.020 | 0.115 |
| Scholarly communication | 0.019 | 0.048 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".