Impact of quitline services on tobacco cessation: an application of modern epidemiologic methods
Bibliographic record
Abstract
This study investigated the effectiveness of quitline service intensity (high vs low) on past 30-day tobacco abstinence at 7-months' follow-up, using observational data from the Oklahoma Tobacco Helpline (OTH) between April 2020 and December 2021. To assess the impact of loss to follow-up and nonrandom treatment assignment, we fit the parameters of a marginal structural model to estimate inverse probability weights for censoring (IPCW), treatment (IPTW), and combined (IPCTW). The risk ratio (RR) was estimated using modified Poisson regression with robust variance estimator. Of the 4695 individuals included in the study, 64% received high-intensity cessation services, and 53% were lost to follow-up. Using the conventional complete case analysis (responders only), high-intensity cessation services were associated with abstinence (RR = 1.18; 95% CI, 1.04-1.34). The effect estimate was attenuated after accounting for censoring (RR = 1.14; 95% CI, 1.00-1.30). After adjusting for both baseline confounding and selection bias via IPTCW, high-intensity cessation services were associated with 1.23 times (95% CI, 1.08-1.41) the probability of abstinence compared to low-intensity services. Despite relatively high loss to follow-up, accounting for selection bias and confounding did not notably impact quit rates or the relationship between intensity of quitline services and tobacco cessation among OTH participants.
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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 | 0.060 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".