Bibliographic record
Abstract
The cost of drugs in America has become so high that those who can afford them complain, and those who cannot afford them sometimes go without. Not a week passes without some newspaper commenting about high drug prices and exploding hospital or state budgets. Many people complain about the excessive cost of the drugs required to treat their rheumatoid arthritis, their depression, hypertension, or peptic ulcers; or grumble about the high price of eradicating their Helicobacter infection; but all this pales into insignificance compared with the price of some of the new anticancer or immunomodulating drugs. There has been much talk about reforms, new legislation, about importing less expensive drugs from Canada or other countries, but so far precious little has happened, and the people are upset. To these complaints the drug makers counter by arguing that developing a new drug and getting it on to the market is expensive ($500m/£313m/€475m per successful drug), and that the shareholders are demanding, the reviewing bureaucracy ponderous and capricious, the required investment large, and the risk of failure high. They remind us that the new drugs have cured many once fatal diseases and revolutionised modern medicine, and suggest that we should take care not to kill the goose that has laid so many golden eggs. Yet existing data indicate that in 1998 the largest 10 drug companies spent only 11.1% of their sales revenue on research and development. Moreover, according to Prescrire, the outspoken Parisian review of drugs, only 15% of new drugs are really innovative, most products being “me too” drugs. Traditionally doctors have been able to save their patients money by prescribing generic drugs. But in 2002 the price of generic drugs rose twice as fast as that of brand name drugs. Within a few years the price of a commonly used antihypertensive drug has risen tenfold, and for some generic medicines the cost has risen by 1000%. Yet such drugs require no research, no massive investment; they merely feed an extensive food chain of manufacturers and middle men—wholesalers, managed care distributors, and pharmacies—geese of a different feather that to our knowledge have not laid any particularly golden eggs.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.245 | 0.102 |
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".