Pricing Pharmaceuticals in a World Environment
Bibliographic record
Abstract
Abstract International comparisons of pharmaceutical prices are playing an increasing role in public policy toward the pharmaceutical industry. Many countries including Italy, Spain, Portugal, and Canada refer to prices of drugs in other countries when setting allowable prices in their own country. In the United States, the Congress and many other consumer organizations have been eager to know if U.S. drug prices are higher than those in other industrial countries. Although earlier studies (e.g., Reekie 1984, Schut and Van Bergeijk 1986, Szuba 1986, Pharmacy Freedom Fund 1990, U.S. Department of Health and Human Services [USDHHS] 1990) have indicated that prescription drug prices are generally higher in the United States than in foreign countries, these studies have been criticized for methodological shortcomings, leading some to discount their conclusions. In the early 1990s, the Congress requested the Government Accountability Office (GAO) to compare ex-manufacturer prices in the United States to those of drugs sold in Canada and the United Kingdom. The GAO found significant price differences at the manufacturers’ level between the United States and these other countries. In fact, all frequently dispensed prescription drugs included in their analyses were priced higher in the United States than they were in the United Kingdom and Canada. The GAO reports had an explosive effect on both the Congress and the pharmaceutical industry. Upon receiving the first report on the U.S.-Canada comparison in early 1992, a hearing was immediately held before the Subcommittee on Health and the Environment of the Committee on Energy and Commerce in the House of Representatives. Legislative bills targeted at regulating prescription drug prices were proposed at once, including the Prescription Drug Prices Review Board Act of 1993, sponsored by Congressman Fortney Stark (D-CA).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".