Integrating Tobacco Use Assessment and Treatment in the Oncology Setting: Quality Improvement Results from the Georgetown Lombardi Smoking Treatment and Recovery Program
Bibliographic record
Abstract
As part of the NCI's Cancer Center Cessation (C3i) initiative, we initiated, expanded, and maintained an evidence-based tobacco treatment program at the Georgetown Lombardi Comprehensive Cancer Center. We present a quality improvement (QI) assessment of the implementation process and patient-level outcomes. At two hematology/oncology outpatient clinical sites, five oncology-based teams (clinical administrators, clinical staff, pharmacy, information technology, and tobacco treatment staff) developed implementation strategies for opt-out patient assessment and enrollment, centralized tobacco treatment, audit, feedback, and staff training. Among eligible patients (tobacco use in ≤30 days), we assessed demographic, clinical, and tobacco-related characteristics to examine predictors of enrollment (baseline completed), treatment engagement (≥one sessions completed), and self-reported 7-day abstinence (6 months post-enrollment). Across both sites, medical assistants screened 19,344 (82.4%) patients for tobacco use, which identified 1345 (7.0%) current tobacco users, in addition to 213 clinician referrals. Of the 687/1256 (54.7%) eligible patients reached, 301 (43.8%) enrolled, and 199 (29.0%) engaged in treatment, of whom 74.5% were African American and 68% were female. At the larger site, significant multivariate predictors of enrollment included African American race (vs. white/other) and clinician referral (vs. MA assessment). Treatment engagement was predicted by greater nicotine dependence, and abstinence (27.4%) was predicted by greater treatment engagement. In summary, the systematic utilization of multiple oncology-based teams and implementation strategies resulted in the development and maintenance of a high-quality, population-based approach to tobacco treatment. Importantly, these strategies addressed inequities in tobacco treatment, as the program reached and engaged a majority-African-American patient population. Finally, the opt-out patient assessment strategy has been implemented in multiple oncology settings at MedStar Health through the Commission on Cancer's Just Ask program.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".