Response to “Rapid Nicotine Ratio and Successful Quitting: Acceptable Explanation” by the Authors of “Nicotine Metabolite Ratio (NMR) Prospectively Predicts Smoking Relapse: Longitudinal Findings from ITC Surveys in Five Countries”
Bibliographic record
Abstract
Dr Kawada’s letter1 raised concerns about our manuscript titled: “Nicotine metabolite ratio (NMR) prospectively predicts smoking relapse: Longitudinal findings from ITC surveys in five countries.”2 These are reasonable criticisms, several of which were acknowledged in the discussion section of the manuscript. We do not have data adequate to address the main points raised in the letter having to do with sample size, salivary cotinine measurement at follow-up, and information on alcohol consumption. We encourage others to attempt to replicate the findings and address limitations noted, especially since our results were opposite of what the hypothesized association was to be. The Population Assessment of Tobacco and Health (PATH) Study has a large representative cohort of US smokers, including biomarker data, which could be used to address many of the limitations in our study. We hope that researchers will use these data to examine whether the findings observed and reported in our paper were an aberration or real. Additionally, these unique findings call for a larger international study that would collect biomarker data to verify smoking status at follow-up and also take into account multiple risk factors (biological/psychosocial/environmental) across multiple countries.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.032 | 0.037 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".