Implementation Science to Improve Tobacco Cessation Services in Oncology Care
Bibliographic record
Abstract
Every patient with cancer deserves access to evidence-based tobacco cessation interventions as part of their routine oncology care. The NCI Cancer Moonshot funded the Cancer Center Cessation Initiative (C3I) to help establish and/or expand tobacco treatment programs at 52 NCI-designated Cancer Centers. Although this initiative has broadened the availability of tobacco treatment services across US cancer centers, the reach and utilization of these services remains low among patients. To help address the remaining gap between the availability and utilization of evidence-based treatments for tobacco use in the oncologic context, staff and investigators at C3I sites and the C3I Coordinating Center formed the C3I Implementation Science Working Group. The mission of this working group is to bring together clinicians, scientists, and policymakers who share a common interest in implementation science and treating tobacco use in the oncologic context to collaborate on projects aimed at shrinking the practice gap in this area. Through case study examples, we describe how the C3I Implementation Science Working Group is supporting efforts to identify effective ways to increase the utilization of evidence-based tobacco treatments within cancer treatment settings and promote the broader impact and long-term sustainability of C3I.
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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 | 0.233 | 0.392 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".