Regulating Prescription Drug Costs
Bibliographic record
Abstract
Prescription drug prices in the United States are notoriously higher than in other high-income countries. Unlike in other countries, the U.S. government does not directly regulate or negotiate the price of drugs. Instead, U.S. drug companies set their own prices, but insurers and pharmacies determine how much patients actually pay out-of-pocket. Critics of U.S. pharmaceutical companies’ drug price inflation claim that the pharmaceutical industry profits from vulnerable patients. Pharmaceutical companies, however, oppose drug pricing reform and argue that lowering the cost of prescription medications will hinder innovation, which could ultimately lead to fewer options for patients. Reducing costs could slow the development of new drugs, but research suggests that slowing innovation may not have a significantly negative effect on patients’ health. In a recent article researchers found that of the 46 new drugs approved in the United States in 2017, 17 provided no or minor additional benefits when compared to existing drugs on the market. Reducing costs may not necessarily inhibit innovation, as both the United Kingdom and Canada enjoy both low drug prices and innovative, profitable pharmaceutical companies. The public has scrutinized the cost of prescription drugs for years, but attempts to pass drug cost legislation have largely failed. The COVID-19 pandemic has only exacerbated the situation as legislation has stalled, the use of prescription drugs has risen, and the price of prescription drugs has increased. In June, pharmaceutical company Gilead Sciences set the price of Remdesivir—the first drug approved to treat COVID-19—at $3,120 per treatment for privately insured patients, although health insurance could lower the out-of-pocket cost for patients. The cost once again sparked criticism of U.S. price setting methods, though the Institute for Clinical and Economic Review calculated that Remdesivir could be cost effective for insurers even if it cost up to $5,080 per treatment. This week’s Saturday Seminar focuses on the regulation of drug costs in the United States.
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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 | 0.015 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
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".