NCCN Framework for Resource Stratification: A Framework for Providing and Improving Global Quality Oncology Care
Bibliographic record
Abstract
More than 14 million new cancer cases and 8.2 million cancer deaths are estimated to occur worldwide on an annual basis. Of these, 57% of new cancer cases and 65% of cancer deaths occur in low- and middle-income countries. Disparities in available resources for health care are enormous and staggering. The WHO estimates that the United States and Canada have 10% of the global burden of disease, 37% of the world's health workers, and more than 50% of the world's financial resources for health; by contrast, the African region has 24% of the global burden of disease, 3% of health workers, and less than 1% of the world's financial resources for health. This disparity is even more extreme with cancer. NCCN has developed a framework for stratifying the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) to help health care systems in providing optimal care for patients with cancer with varying available resources. This framework is modified from a method developed by the Breast Health Global Initiative. The NCCN Framework for Resource Stratification (NCCN Framework) identifies 4 resource environments: basic resources, core resources, enhanced resources, and NCCN Guidelines, and presents the recommendations in a graphic format that always maintains the context of the NCCN Guidelines. This article describes the rationale for resource-stratified guidelines and the methodology for developing the NCCN Framework, using a portion of the NCCN Cervical Cancer Guideline as an example.
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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 | 0.181 | 0.189 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.015 | 0.028 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".