Abstract PO-058: Piloting a novel strategy to rapidly implement smoking cessation treatment for newly-diagnosed head and neck cancer patients
Bibliographic record
Abstract
Abstract Purpose The purpose of this study is to evaluate a novel, bimodal strategy to implement smoking cessation treatment prior to oncologic therapy in newly-diagnosed head and neck cancer (HNC) patients. Background Among the 65,000 people who develop HNC in the United States (US) annually, 20-30% smoke cigarettes and 50-80% will continue smoking throughout survivorship. Quitting smoking before oncologic therapy correlates with improved health outcomes. However, the window of opportunity to quit smoking is narrow since cancer treatment often begins 4-5 weeks after the first oncology visit. The failure to rapidly implement evidence-based smoking cessation treatment is a major cause of this problem. Offering both behavioral therapy and pharmacotherapy increases abstinence rates by 2-3-fold. Despite this, only 4-17% of cancer patients receive both therapies at any time. Methods We conducted a pragmatic, quasi-experimental, pilot study using a pre-post design to evaluate a strategy to rapidly implement behavioral therapy and pharmacotherapy. The pre-test period was from 1/21-3/22 and the strategy was deployed from 4/22-10/22. The population included newly-diagnosed patients with mucosal HNC or salivary gland cancer. The setting was an academic medical center with an established tobacco treatment program (TTP). The implementation strategy involved bimodal administration of the evidence-based Ask, Advise, Connect (AAC) approach before and at the first surgical oncology visit. First, a dedicated medical assistant (MA) used an electronic health record (EHR)-based tool to identify, ask, advise, and connect (i.e. refer) smokers to the TTP at the time of clinic referral, or ~1-2 weeks before the first surgical oncology visit. Second, the AAC strategy was delivered by the triage MA and nurse at the time of the first surgical oncology visit. Some surgeons opportunistically offered patients pharmacotherapy. Results Among the 383 eligible, newly-diagnosed HNC patients, and 48 (12.5%) were current smokers. Twelve smokers were diagnosed between 4/22-10/22 and were eligible for the bimodal AAC strategy. However, only six of 12 patients received the AAC strategy before the first oncology visit. Among the 48 smokers, 75% were male, the median age was 65.5-years, and cancer treatment was started a median 34 days after the first oncology visit. Within 30 days of the first surgical oncology visit, 67% were advised to quit, 25% were referred to the TTP, 6% completed a comprehensive TTP visit, 21% had pharmacotherapy ordered, and 17% received both behavioral therapy and pharmacotherapy. While there were otherwise no differences in outcomes between pre- and post-strategy groups, more bimodal AAC strategy-eligible patients received pharmacotherapy (42%) compared to pre-strategy patients (14%, p=0.040). Conclusions Our bimodal AAC strategy correlated with increased use of pharmacotherapy, despite suboptimal fidelity to the approach. Additional strategies to improve delivery of timely behavioral therapy and pharmacotherapy for smoking cessation are needed. Citation Format: Sahajveer Mann, Julia Casazza, Dalia Mitchell, Quynh-Chi Dang, Dequan Weston, Brette Harding, Baran D. Sumer, Brittny Tillman, Heather Kitzman, George Jackson, Robert Schnoll, Amit Singal, Andrew T. Day. Piloting a novel strategy to rapidly implement smoking cessation treatment for newly-diagnosed head and neck cancer patients [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-058.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".