An Overview of the U.S. National Cancer Institute's (NCI) 2023 International Research Portfolio
Bibliographic record
Abstract
PURPOSE As part of its goal to represent NCI and promote its engagement with international partners, the NCI Center for Global Health (CGH) conducts an annual analysis of NCI's international research portfolio, which includes NCI-funded extramural grants and NCI intramural projects with principal investigators (PIs) or collaborators at institutions outside of the U.S. This annual analysis gives insight to how NCI supports global oncology and informs global research opportunities. METHODS NCI CGH fielded a data call within NCI's two divisions that conduct intramural research. PIs reported intramural projects with active international collaborators in fiscal year (FY) 2023. NCI-funded extramural grants active in FY23 with any foreign collaborator were identified in NIH's internal database, Query-View-Report. Grants were categorized by common scientific outline (CSO) code and cancer site according to Dimensions for NIH and the International Cancer Research Partnership (ICRP). Analysis was conducted in Microsoft Excel and Python. RESULTS NCI funded 1,075 extramural research grants and 273 intramural research projects with international collaborators in FY23, including 248 extramural grants and 44 intramural projects initiated in the past year. These grants included collaborators at 1,633 international institutions in 117 countries. Forty-seven extramural grants were direct awards to international institutions across 16 countries, 9 of which are low- and middle-income countries (LMICs). The intramural research projects included collaborators at 478 institutions across 58 countries. The analysis presents details of trends in geographic, income groups, cancer site, and CSO code distribution of the identified grants and projects. CONCLUSION The NCI supports a broad and growing portfolio of research with international collaborators. While the majority of research includes collaborators in Canada and Europe, there is increasing collaboration with institutions in LMICs. This analysis continues to aid our understanding of existing collaborations and identify gaps in research funding and training.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".