Bibliographic record
Abstract
The pharmaceutical industry and its pricing methods provide an inviting target, easy to disparage and caricature. Even after accounting for discounts and rebates, average prices of leading brand-name drugs in the United States are two to four times higher than they are in Canada, Japan, and many European countries.1 US per capita spending on prescription drugs is more than twice the level in the United Kingdom.2,3 Prices for most new cancer drugs now exceed $100,000 per patient per year of treatment, despite the fact that many of these treatments seem to offer modest gains in life expectancy or lack such evidence at all.4,5 With the advent of ever more targeted and powerful treatments, including cell- and gene-based therapies with multimillion dollar price tags, the need for sensible drug pricing and coverage policies will intensify. Despite the controversies, there are few if any books dedicated to the question of what is a “fair” or “reasonable” drug price and how to think about the question. To be sure, the bookshelves fairly groan with works critical of pharmaceutical company practices, asserting or implying that drugs often deliver poor value for the money. Notable examples in the past decade or so include Protecting America’s Health6; Overdosed America: The Broken Promise of American Medicine7; Powerful Medicines: The Benefits, Risks, and Costs of Prescription Drugs8; The Truth About Drug Companies: How They Deceive Us and What to Do About It9; Selling Sickness: How the World’s Biggest Pharmaceutical Companies Are Turning Us All Into Patients10; Bad Pharma: How Drug Companies Mislead Doctors and Harm Patients11; Ending Medical Reversal: Improving Outcomes, Saving Lives12; An American Sickness: How Healthcare Became Big Business and How You Can Take it Back13; and PhRMA: Greed, Lies and the Poisoning of America.14 Such accounts tend to highlight examples of disreputable behaviors and devote considerable attention to industry-wide reforms, rather than how to consider the costs and benefits of individual drug therapies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.738 | 0.583 |
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".