Imaging in antiangiogenesis trial: a clinical trials radiology perspective
Bibliographic record
Abstract
Traditional approaches for treating cancer have largely focused on the ability of chemotherapy, and to a lesser extent radiation therapy, to destroy tumour cells. Recent developments in antiangiogenesis treatments require a fundamental shift in the radiological and imaging paradigms associated with evaluating response. Proper design and execution of any clinical trial involving imaging angiogenesis requires satisfactory consideration of a number of strategies and an in-depth understanding of different imaging techniques such as dynamic contrast enhanced MRI and CT, contrast-enhanced ultrasound and positron emission tomography. In particular, for imaging, the strategies can be divided into issues that need to be addressed during the protocol planning phase, and strategies that need to be addressed during the execution phase. Furthermore, clinical trials are usually subject to stringent regulations surrounding traceability and reproducibility that need to be followed before the regulatory authorities will accept the integrity of the data. This paper elaborates on the above strategies and outlines certain aspects, or tactics, that need to be considered while preparing for a multicentre clinical trial that involves imaging angiogenesis.
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 | 0.027 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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 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".