Improving Cancer Outcomes Through International Collaboration in Academic Cancer Treatment Trials
Bibliographic record
Abstract
PURPOSE: The need for international collaboration in cancer clinical trials has grown stronger as we have made progress both in cancer treatment and screening. We sought to identify those efforts already underway which facilitate such collaboration, as well as barriers to greater collaboration. METHODS: We reviewed the collective experiences of many cooperative groups, governmental organizations, nongovernmental organizations, and academic investigators in their work to build international collaboration in cancer clinical trials across multiple disease sites. RESULTS: More than a decade of work has led to effective global harmonization for many of the elements critical to cancer clinical trials. Many barriers remain, but effective international collaboration in academic cancer treatment trials should become the norm, rather than the exception. CONCLUSION: Our ability to strengthen international collaborations will result in maximization of our resources and patients, permitting us to change practice by establishing more effective therapeutic strategies. Regulatory, logistical, and financial hurdles, however, often hamper the conduct of joint trials. We must work together as a global community to overcome these barriers so that we may continue to improve cancer treatment for patients around the world.
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 | 0.040 | 0.212 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.005 | 0.011 |
| 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; both teacher heads agree on what is shown here.
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".