Efficacy of Emergency Department-Initiated Tobacco Control--Systematic Review and Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
AIM: Systematic review and meta-analysis of randomized controlled trials evaluating the efficacy of emergency department-initiated tobacco control (ETC). METHODS: Literature search in 7 databases and gray literature sources. Point prevalence tobacco abstinence at 1-, 3-, 6-, and/or 12-month follow-up was abstracted from each study. The proportionate effect (relative risk) of ETC on tobacco abstinence was calculated separately for each study and follow-up time and pooled, at different follow-up times, by Mantel-Haenszel relative risks. The effects of ETC on combined point prevalence tobacco abstinence across all follow-up times were calculated using generalized linear mixed models. RESULTS: Seven studies with overall 1,986 participants were included. The strongest effect of ETC on point prevalence tobacco abstinence was found at 1 month: Relative risk (RR) = 1.47 (3 studies) (95% confidence interval [CI]: 1.06-2.06), while the effect at 3, 6, and 12 months was RR = 1.24 (6 studies) (95% CI: 0.93-1.65); 1.13 (5 studies) (95% CI: 0.86-1.49); and 1.25 (1 study) (95% CI: 0.91-1.72). The benefit on combined point prevalence tobacco abstinence was RR = 1.33 (7 studies) (95% CI: 0.96-1.83), p = .08; with RR = 1.33 (95% CI: 0.92-1.92), p = .10, for the 5 studies combining motivational interviewing and booster phone calls. CONCLUSIONS: ETC combining motivational interviewing and booster phone calls showed a trend toward increased episodically measured tobacco abstinence up to 12 months. More methodologically rigorous trials are needed to effectively evaluate the impact of ETC.
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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 | 0.024 | 0.066 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.040 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".