Cessation of smoking trial in the emergency department (CoSTED): protocol for a multicentre randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Attendees of emergency departments (EDs) have a higher than expected prevalence of smoking. ED attendance may be a good opportunity to prompt positive behaviour change, even for smokers not currently motivated to quit. This study aims to determine whether an opportunist smoking cessation intervention delivered in the ED can help daily smokers attending the ED quit smoking and is cost-effective. METHODS AND ANALYSIS: A two-arm pragmatic, multicentred, parallel-group, individually randomised, controlled superiority trial with an internal pilot, economic evaluation and mixed methods process evaluation. The trial will compare ED-based brief smoking cessation advice, including provision of an e-cigarette and referral to local stop smoking services (intervention) with the provision of contact details for local stop smoking services (control). Target sample size is 972, recruiting across 6 National Health Service EDs in England and Scotland. Outcomes will be collected at 1, 3 and 6 months. The primary outcome at 6 months is carbon monoxide verified continuous smoking abstinence. ETHICS AND DISSEMINATION: The trial was approved by the South Central-Oxford B Research Committee (21/SC/0288). Dissemination will include the publication of outcomes, and the process and economic evaluations in peer-reviewed journals. The findings will also be appropriately disseminated to relevant practice, policy and patient representative groups. TRIAL REGISTRATION NUMBER: NCT04854616; protocol V.4.2.
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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 | 0.044 | 0.040 |
| Meta-epidemiology (narrow) | 0.009 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.118 | 0.018 |
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".