Desperately seeking cancer drugs: explaining the emergence and outcomes of accelerated pharmaceutical regulation
Bibliographic record
Abstract
Government regulators have increasingly accelerated new cancer drugs on to the market by granting them approval based on less clinical data supporting drug efficacy than permitted under standard regulations. With more lenient regulatory standards, pharmaceutical companies have keenly sought to develop cancer drugs. Focusing on the US, this article examines how the emergence and implementation of such accelerated approvals should be understood, particularly in relation to corporate bias and disease-politics theories. Drawing on longitudinal and case study data analysis, it is argued that the emergence of accelerated approval regulations for cancer drugs should be regarded primarily as part of a deregulatory regime driven by the interests of the pharmaceutical industry in partnership with all major aspects of the state, rather than as a response to patient activism in the aftermath of AIDS. Furthermore, even in cases when some patients successfully demand accelerated marketing approval of cancer drugs, such approval by regulators, while in manufacturers' interests, may not be in the interests of patients' health because the political culture of the regulatory agency is reluctant to uphold its own techno-regulatory standards of public-health protection when that would challenge the agenda-setting influence of manufacturers, including industry collaborations with patients and the medical profession.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".