Children's Oncology Group 2023 blueprint for research: Cancer care delivery research
Bibliographic record
Abstract
The National Cancer Institute (NCI) has a 40-year history of initiatives to encourage the participation of community oncology sites into clinical trials research and clinical care. In 2014, the NCI re-organized to form the NCI Community Oncology Research Program (NCORP) network across seven research bases, including the Children's Oncology Group (COG), and numerous community sites. The COG portfolio for Cancer Care Delivery Research (CCDR), mirroring the larger NCORP network, has included two studies addressing guideline congruence, as an important marker of quality cancer care, and another focusing on financial toxicity, addressing the pervasive problems of healthcare cost. CCDR is a cross-cutting field that frequently examines intersectional aspects of healthcare delivery. With that in mind, we explicitly define domains of CCDR to propel our research agenda into the next phase of the NCORP CCDR program while acknowledging the complex and dynamic fields of clinical care, policy level decisions, research findings, and needs of communities served by the NCORP network that will inform the subsequent research questions. To ensure programmatic success, we will engage a broad interdisciplinary group of investigators and clinicians with expertise and dedication to community oncology and the populations they serve.
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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 | 0.131 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.025 | 0.030 |
| Insufficient payload (model declined to judge) | 0.043 | 0.029 |
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".