Implementing a Metabolism-informed approach for smoking cessation in an Alaska Tribal health system: study protocol for a single-arm implementation pilot trial
Bibliographic record
Abstract
BACKGROUND: Individualized treatment for commercial tobacco smoking cessation, such as through the utilization of the nicotine metabolite ratio (NMR), offers potential clinical benefit. NMR is a metabolism-informed biomarker that can be used to guide medication selection. NMR testing is particularly promising for tobacco cessation efforts in populations with high rates of smoking, such as some Alaska Native and American Indian (AN/AI) communities. To date, no prior study has evaluated the implementation of NMR-guided tobacco cessation with AN/AI populations. METHODS: The present "QUIT" protocol is a two-phase study that will occur at Southcentral Foundation (SCF), an Alaska Native-owned health system, serving 70,000 AN/AI people, based in Anchorage, Alaska. In Phase one, qualitative interviews with customer-owners (patients), providers and administrators (n = 36) and a 10-participant beta-test will be used to refine a strategy to implement NMR testing in the health system. Phase two will involve a single-arm pilot trial (n = 50) and qualitative interviews throughout data collection (n = 48) to evaluate the implementation strategy and explore the real-world acceptability and feasibility of NMR testing to guide tobacco cessation with AN/AI populations. DISCUSSION: This study utilizes a community-based participatory approach to refine and implement a nicotine metabolism-informed smoking cessation program in a Tribal healthcare setting. The process and findings from this study will reflect the importance of customer-owner choice and honor the lived experience involved in quitting commercial tobacco. Pilot study data will inform the effect and sample sizes required for a future pragmatic trial of NMR-guided smoking cessation. TRIAL REGISTRATION: This study will be registered with clinicaltrials.gov after the beta test is complete and the final IRB protocol is approved.
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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 | 0.051 | 0.043 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.066 | 0.013 |
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".