Evaluating the impact of varying expired carbon monoxide thresholds on smoking relapse identification: insights from the E3 trial on e-cigarette efficacy for smoking cessation
Bibliographic record
Abstract
OBJECTIVES: Expired carbon monoxide (ECO) is often used in smoking cessation trials to biochemically validate self-reported smoking status. The optimal ECO threshold to distinguish individuals who smoke from those who do not is debated. DESIGN: The data from the 'Evaluating the Efficacy of E-Cigarette use for Smoking Cessation (E3) Trial' were used; the E3 trial was a randomised controlled trial that examined e-cigarettes efficacy for smoking cessation. SETTINGS: Participants were recruited from 17 Canadian sites across 4 provinces. PARTICIPANTS: This substudy included data from participants who returned for at least one of the clinical visits at week 4 (291), 12 (257) or 24 (218) and provided both self-reported smoking status and ECO measures. Analyses were based on 766 paired measures (ie, self-reported smoking status with corresponding ECO). RESULTS: The ability of ECO measurements to discriminate between adults who reported smoking and those who reported abstinence varied with the threshold used. ECO thresholds of 6, 7, 8 and 9 parts per million (ppm) yielded the greatest area under the receiver operating characteristic curve (0.84). These thresholds produced sensitivities of 84%, 82%, 78% and 76% and specificities of 84%, 87%, 90% and 91%, respectively. However, at a threshold of 6 ppm, intersecting sensitivity (84%) and specificity (84%) were maximised with respect to each other. Biochemical validation had the highest agreement with self-report at an ECO threshold of 6 ppm (κ=0.57; 95% CI, 0.51 to 0.64). CONCLUSION: The classification of participants' smoking status depends on the ECO threshold used for biochemical validation. We recommend that future smoking cessation trial investigators analyse and report the impact that varying ECO thresholds has on trial results. TRIAL REGISTRATION NUMBER: NCT02417467.
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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 | 0.041 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".