Yorkshire Enhanced Stop Smoking (YESS) study: a protocol for a randomised controlled trial to evaluate the effect of adding a personalised smoking cessation intervention to a lung cancer screening programme
Bibliographic record
Abstract
INTRODUCTION: Integration of smoking cessation (SC) into lung cancer screening is essential to optimise clinical and cost effectiveness. The most effective way to use this 'teachable moment' is unclear. The Yorkshire Enhanced Stop Smoking study will measure the effectiveness of an SC service integrated within the Yorkshire Lung Screening Trial (YLST) and will test the efficacy of a personalised SC intervention, incorporating incidental findings detected on the low-dose CT scan performed as part of YLST. METHODS AND ANALYSIS: Unless explicitly declined, all smokers enrolled in YLST will see an SC practitioner at baseline and receive SC support over 4 weeks comprising behavioural support, pharmacotherapy and/or a commercially available e-cigarette. Eligible smokers will be randomised (1:1 in permuted blocks of random size up to size 6) to receive either an enhanced, personalised SC support package, including CT scan images, or continued standard best practice. Anticipated recruitment is 1040 smokers (January 2019-December 2020). The primary objective is to measure 7-day point prevalent carbon monoxide (CO) validated SC after 3 months. Secondary outcomes include CO validated cessation at 4 weeks and 12 months, self-reported continuous cessation at 4 weeks, 3 months and 12 months, attempts to quit smoking and changes in psychological variables, including perceived risk of lung cancer, motivation to quit smoking tobacco, confidence and efficacy beliefs (self and response) at all follow-up points. A process evaluation will explore under which circumstances and on which groups the intervention works best, test intervention fidelity and theory test the mechanisms of intervention impact. ETHICS AND DISSEMINATION: This study has been approved by the East Midlands-Derby Research Ethics Committee (18/EM/0199) and the Health Research Authority/Health and Care Research Wales. Results will be disseminated through publication in peer-reviewed scientific journals, presentation at conferences and via the YLST website. TRIAL REGISTRATION NUMBERS: ISRCTN63825779, NCT03750110.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.034 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.086 | 0.016 |
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".