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Record W3045773104 · doi:10.1017/cts.2020.323

4199 A pilot randomized controlled trial of precision care for smoking cessation in the Southern Community Cohort Study

2020· article· en· W3045773104 on OpenAlexaff
Nicole Senft, Maureen Sanderson, Rebecca Selove, William J. Blot, Rachel F. Tyndale, Quiyin Cai, Karen Gilliam, Suman Kundu, Hilary A. Tindle

Bibliographic record

VenueJournal of Clinical and Translational Science · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineSmoking cessationVareniclineNicotineCohortPopulationLung cancerMedical prescriptionInternal medicineRandomized controlled trialNicotine replacement therapyPharmacologyEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVES/GOALS: Precision care may engage smokers and providers in treatment but is understudied in the community. We piloted guideline-based care (GBC) alone or with Respiragene, a lung cancer polygenic risk score (PRS, 1-10), or metabolism-informed choice of medication using the nicotine metabolite ratio (NMR). METHODS/STUDY POPULATION: Daily smokers (n = 58) with stored biospecimens in the Southern Community Cohort Study were randomized 1:1:1 to GBC, PRS, or NMR, counseled to quit smoking, and co-selected FDA-approved cessation medication (nicotine replacement, varenicline) with a tobacco counselor. In PRS, precision motivational counseling was guided by PRS (i.e., lung cancer risk 10-40-fold that of never-smokers). In NMR, precision medication recommendations consisted of varenicline for faster metabolizers (NMR≥0.31) and nicotine replacement for slow metabolizers (NMR<0.31). Feasibility was defined as achieving at least 50% provider engagement (med prescription) and at least 50% patient engagement (self-reported med use). RESULTS/ANTICIPATED RESULTS: Participants were median age 59, 72% female, 81% Black, 60% with incomes <$15,000; median cigarettes/day was 15 (IQR 8-20) and 52% reported time-to-first cigarette <5 minutes, illustrating moderate nicotine dependence. Providers confirmed medication prescriptions for 40% of patients (32% GBC, 50% PRS, 37% NMR) and 83% of patients reported using medication (prescribed or unprescribed) during the study (90% GBC, 80% PRS, 79% NMR). At 6-month follow-up, 27% (n = 15) reported cessation (39% GBC, 16% PRS, 26% NMR). Among persistent smokers, 46% reported smoking at least 50% fewer cigarettes/day compared to baseline (45% GBC, 38% PRS, 57% NMR). Small sample size precluded statistical comparisons. DISCUSSION/SIGNIFICANCE OF IMPACT: Precision interventions to quit smoking are feasible for community smokers, who engaged at high rates. However, only 40% of providers supported patients’ quit attempts with medication prescriptions. Future research should test strategies to raise provider engagement in precision smoking treatment. CONFLICT OF INTEREST DESCRIPTION: R.F.T. has consulted for Quinn Emmanual and Apotex on unrelated topics. H.A.T. reported providing input on design for a phase 3 trial of cytisine proposed by Achieve Life Sciences and being a principal investigator of National Institutes of Health–sponsored studies for smoking cessation that include medications donated by the manufacturers. Other authors declare no potential conflicts of interest.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.074
GPT teacher head0.403
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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