Bibliographic record
Abstract
The concept of treating solid tumors by inhibiting tumor angiogenesis was first articulated almost 30 years ago. For the next 10 years it attracted little scientific interest. This situation changed, relatively slowly, over the succeeding decade with the discovery of the first pro-angiogenic molecules such as basic fibroblast growth factor and vascular endothelial growth factor (VEGF), and the development of methods of successfully growing vascular endothelial cells in culture as well as in vivo assays of angiogenesis. However, the 1990s have witnessed a striking change in both attitude and interest in tumor angiogenesis and anti-angiogenic drug development, to the point where a remarkably diverse group of over 24 such drugs is currently undergoing evaluation in phase I, II or III clinical trials. In this review I will discuss the many reasons for this. These features, together with other recent discoveries have created intense interest in initiating and expanding anti-angiogenic drug discovery programs in both academia and industry, and the testing of such newly developed drugs, either alone, or in various combinations with conventional cytotoxic therapeutics. However, significant problems remain in the clinical application of angiogenesis inhibitors such as the need for surrogate markers to monitor the effects of such drugs when they do not cause tumor regressions, and the design of clinical trials. Also of concern is that the expected need to use anti-angiogenic drugs chronically will lead to delayed toxic side effects in humans, which do not appear in rodents, especially in short-term studies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".