Test-Retest Reliability and Stability of the Nicotine Metabolite Ratio Among Treatment-Seeking Smokers
Bibliographic record
Abstract
INTRODUCTION: The nicotine metabolite ratio (NMR), the ratio of 3-hydroxycotinine to cotinine, is a biomarker used in smoking cessation research, with several retrospective studies suggesting that NMR predicts treatment outcome. To be maximally useful in tailoring treatment, estimates of NMR should be stable over time. The present study is the first to examine the short-term test-retest reliability of NMR among treatment-seeking smokers. METHODS: Blood NMR was assessed at two time points, approximately 2-3 weeks apart and prior to intervention, among 72 healthy adult smokers (49% female; 35% non-White) enrolled in a cessation trial (http://ClinicalTrials.gov ID: NCT01314001). RESULTS: Mean NMR was stable from Time-1 to Time-2, with no significant change between assessments; test-retest reliability for NMR values was excellent (ICC[2,1] = 0.87). Test-retest reliability remained acceptable to high when NMR was categorized, as in recent clinical trials. Classification of participants as slow (quartile 1, NMR ≤ 0.24) or normal/fast NMR (quartiles 2-4, NMR ≥ 0.25) was consistent from Time-1 to Time-2 for 96% of participants (κ = 0.89). Though classification of participants into NMR quartiles was less consistent from Time-1 to Time-2 (67% agreement; weighted κ = 0.73), all reclassifications occurred between adjacent quartiles. CONCLUSIONS: Overall, these data support the use of a single NMR assessment for association studies with smoking phenotypes and in smokers seeking to quit, and they encourage large-scale efforts to determine optimal NMR cutpoints for tailoring treatment selection.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".