Cancer Groundshot: Building a Robust Cancer Control Platform in Addition To Launching the Cancer Moonshot
Bibliographic record
Abstract
Cancer Groundshot is a philosophy that calls for prioritization of strategies in global cancer control. The underlying principle of Cancer Groundshot is that one must ensure access to interventions that are already proven to work before focusing on the development of new interventions. In this article, we discuss the philosophy of Cancer Groundshot as it pertains to priorities in cancer care and research in low- and middle-income countries and the utility of technology in addressing global cancer disparities; we also address disparities seen in high-income countries. The oncology community needs to realign our priorities and focus on improving access to high-value cancer control strategies, rather than allocating resources primarily to the development of technologies that provide only marginal gains at a high cost. There are several "low-hanging fruit" actions that will improve access to quality cancer care in low- and middle-income countries and in high-income countries. Worldwide, cancer morbidity and mortality can be averted by implementing highly effective, low-cost interventions that are already known to work, rather than investing in the development of resource-intensive interventions to which most patients will not have access (i.e., we can use Cancer Groundshot to first save more lives before we focus on the "moonshots").
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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 | 0.017 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.062 | 0.017 |
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".