Analyses of repeated failures in cancer therapy for solid tumors: poor tumor‐selective drug delivery, low therapeutic efficacy and unsustainable costs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For over six decades reductionist approaches to cancer chemotherapies including recent immunotherapy for solid tumors produced outcome failure-rates of 90% (±5) according to governmental agencies and industry. Despite tremendous public and private funding and initial enthusiasm about missile-therapy for site-specific cancers, molecular targeting drugs for specific enzymes such as kinases or inhibitors of growth factor receptors, the outcomes are very bleak and disappointing. Major scientific reasons for repeated failures of such therapeutic approaches are attributed to reductionist approaches to research and infinite numbers of genetic mutations in chaotic molecular environment of solid tumors that are bases of drug development. Safety and efficacy of candidate drugs tested in test tubes or experimental tumor models of rats or mice are usually evaluated and approved by FDA. Cost-benefit ratios of such 'targeted' therapies are also far from ideal as compared with antibiotics half a century ago. Such alarming records of failure of clinical outcomes, the increased publicity for specific vaccines (e.g., HPV or flu) targeting young and old populations, along with increasing rise of cancer incidence and death created huge and unsustainable cost to the public around the globe. This article discusses a closer scientific assessment of current cancer therapeutics and vaccines. We also present future logical approaches to cancer research and therapy and vaccines.
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.
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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it