The ICGC ARGO data dictionary for standardizing global cancer clinical data
Bibliographic record
Abstract
The International Cancer Genome Consortium Accelerating Research in Genomic Oncology (ICGC ARGO) project is an international initiative to sequence germline and tumour genomes from 100,000 cancer patients across 13 countries and 22 tumour types. By integrating genomic data with comprehensive clinical information including treatment outcomes, lifestyle, environmental exposures and family history, ICGC ARGO aims to accelerate the application of genomic insights in cancer diagnosis, treatment and prevention. However, a major challenge is harmonizing clinical data from diverse tumour types worldwide. To address this, the ICGC ARGO Data Dictionary was developed to ensure consistent high-quality clinical data collection by defining a minimal set of clinical fields within an event-based data model to capture clinical relationships and support longitudinal data collection. Grounded in international standardized terminology, it is interoperable with other data standards such as Minimal Common Oncology Data Elements (mCODE). Its adoption by global initiatives such as the European-Canadian Cancer Network (EUCANCan) and the Marathon of Hope Cancer Centres Network (MOHCCN) underscores its broad impact on advancing precision oncology research.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.010 |
| 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 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".