An Arm and a Leg: The Rising Cost of Cancer Drugs and Impact on Access
Bibliographic record
Abstract
Increasing cancer drug prices present global challenges to treatment access and cancer outcomes. Substantial variability exists in drug pricing across countries. In countries without universal health care, patients are responsible for treatment costs. Low- or middle-income countries are heavily impacted, with limited patient access to novel cancer treatments. Financial toxicity is seen across cancer types, countries, and health care systems. Those at highest risk include younger patients, new immigrants, visible minority groups, and those without private health coverage. Currently, cancer drug pricing does not correlate with value or clinical benefit. Value-based pricing of oncology drugs may incentivize development of higher-value medicines and eliminate excess spending on drugs that yield little benefit. Generics and biosimilars in oncology can also improve affordability and patient access, offering dramatic reductions in drug spending while maintaining patient benefit. Oncologists can promote value-based care by following evidence-based clinical guidelines that avoid low-value treatments. Researchers can also engage in value-based research that critically explores optimal cancer drug dosing, schedules, and treatment duration and defines patient populations most likely to benefit (e.g., through biomarker selection). Cancer Groundshot proposes that we improve outcomes for today's patients with cancer, including broader global access for high-value treatments, promotion of affordable cancer control strategies, and reduction of cancer morbidity and mortality through low-cost prevention and screening initiatives. Moving forward, major oncology societies recommend promoting uniform global access to essential cancer medicines and avoiding financial harm for patients as key principles in addressing the affordability of cancer drugs.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".