Bibliographic record
Abstract
Advances in cancer treatment have led to substantial improvements in survival. These include several drugs for treatment of multiple myeloma and metastatic prostate cancer, and immunotherapy with checkpoint inhibitors for treatment of nonsmall cell lung cancer, kidney cancer, melanoma, and others. Management of breast cancer has also changed substantially in the last two decades with approval of CDK4/6 inhibitors, trastuzumab, pertuzumab, TDM1, and other drugs. The pricing of these agents is set by what the market will bear but typically will be more than U.S. $12,000 (~INR 880,000) per month for approved schedules of treatment in the United States, an obscene price that brings huge profits to the pharmaceutical industry. In India, the cost of these therapies is not affordable and only a handful of patients are treated with these medications. There may be some price reduction in other countries, particularly those with a national health service that can bargain for bulk purchase. Some drugs are manufactured and sold in India at a much lower price. However, many effective drugs remain unaffordable for all but the wealthy in lower- and middle-income countries (LMIC) such as India. The majority of people with cancer who could benefit from treatment with new drugs live in LMIC. It is a hollow victory to have generated effective treatments for several types of cancer, but for these therapies, not to be available to the global majority who could benefit. And the nonavailability of life-prolonging treatment is not due to the cost of manufacturing the drugs, it is due to protection of profit at the expense of human life.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".