Strengthen Medicare: End Drug Company Price Setting
Bibliographic record
Abstract
It’s no secret that, four years ago, President Obama cut a deal with the pharmaceutical industry. He promised that so long as the drug companies did not block health reform, federal law would continue to prohibit Medicare from negotiating drug prices. Instead, the pharmaceutical industry would get 30 million new customers and remain free to set drug prices for Americans. This single policy will cost Medicare and U.S. tax payers hundreds of billions of dollars over the next ten years. If Congress wants to contain long-term Medicare spending and keep health care affordable in America, lawmakers should start with the low-hanging fruit: the excessive prices Medicare and our citizens pay for drugs. Medicare easily pays between 150 and 300 percent of the average cost of prescription drugs in the other wealthy nations. Recently released data from the International Federation of Health Plans make the point. A monthly supply of Lipitor (a common cholesterol medication) costs about $100 in the U.S.; the same drug costs about $6 in New Zealand and $48 in France. Nasonex (commonly prescribed for nasal infections) costs Medicare about $108 for a monthly supply; the same drug costs France $17 and Canada $29. At best, the United States subsidizes the prescription costs of all other wealthy nations; at worst, we are simply dupes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.052 |
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".