Identification of Sociodemographic, Clinical, and Genetic Factors to Aid Alaska Native and American Indian People to Successfully Quit Smoking
Bibliographic record
Abstract
INTRODUCTION: Alaska Native and American Indian (ANAI) people have a smoking prevalence of 23%. Nicotine metabolite ratio (NMR) and genetic testing may enable tailored selection of tobacco cessation medication. AIMS AND METHODS: The purpose of this study was to evaluate the relative contributions of NMR, cessation medication, demographics, and tobacco use history to cessation. Participants were recruited into an observational cohort study consisting of a baseline visit prior to their quit date and 6-week follow-up. Demographic and tobacco use surveys and blood, urine, and breath samples were collected at each visit. Electronic health records were queried for cessation medications. NMR was categorized into slow or normal nicotine metabolism phenotypes (<0.31 and ≥ 0.31, respectively). The main outcome was cessation at 6 weeks. Analyses consisted of descriptive statistics, medication and phenotype concordance, and estimates of relative risk (RR) of quitting. RESULTS: We enrolled 151 ANAI adults who smoked cigarettes daily. Two-thirds had normal nicotine metabolism phenotype. Retrospective medication and phenotype concordance was 39%. The overall quit rate was 25%. No demographic factors or tobacco use history were associated with quit success. Varenicline and bupropion increased the likelihood of quitting (RR = 2.93 [1.42, 6.03] and RR = 2.52 [1.12, 5.64], respectively) compared to nicotine replacement therapy. Non-optimal medication and phenotype concordance decreased likelihood of quit success (RR = 0.44 [0.22, 0.91]) compared to optimal concordance. CONCLUSIONS: This exploratory study found associations between quit success and tobacco cessation medication as well as medication and phenotype concordance. Additional research is needed to assess use of NMR for treatment selection among ANAI people. IMPLICATIONS: These results broadly support additional community-engaged research to improve medication and phenotype concordance in tribal health settings. Such future research on implementing meditcation and phenotype concordance holds promise to improve expectations, quit success, and health outcomes amongst individuals attempting to quit smoking.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".