Allen Carr’s Easyway to Stop Smoking - A randomised clinical trial
Bibliographic record
Abstract
OBJECTIVE: To determine if Allen Carr's Easyway to Stop Smoking (AC) was superior to Quit.ie in a randomised clinical trial (RCT). SETTING: Single centre, open RCT, general population based. PARTICIPANTS: 300 adult smokers, 18 years plus, minimum 5 cigarettes daily, and English speaking. AC, 151 (females 44.4%) and Quit.ie, 149 (females 45.6%), mean age 44 years. outcomes for all 300 were analysed (intention-to-treat). Recruited through advertisement from July 2015 to February 2016. INTERVENTION: Randomly assigned to AC (n=151) and Quit.ie (n=149), matched for age, sex and education. Block randomisation, enrolment and follow-up at 1, 3, 6 and 12 months. Primary aim was to determine if AC had higher quit rates than Quit.ie service at 3 months. Secondary aims: quit rates at 1, 6 and 12 months and analysis of associated factors including weight. AC consisted of a 5-hour seminar, in a group setting. Quit.ie is an online portal for smoking cessation. RESULTS: AC had higher quit rates at 1, 3, 6 and 12 months. AC: 38%, (n=57), 27% (n=40), 23% (n=35), 22% (n=33) vs Quit.ie: 20% (n=30), 15% (n=22), 15% (n=23), 11% (n=17), respectively (all p values <0.05). Logistic regression AC vs Quit.ie, OR 2.26 (95% CI 1.22 to 4.21) p value=0.01. Weight gain 3.8 kg in AC vs 1.8 kg in Quit.ie (p value <0.05). CONCLUSIONS: All AC quit rates were superior to Quit.ie, outcomes were comparable with established interventions. TRIAL REGISTRATION NUMBER: ISRCTN12951013. Recruitment July 2015-February 2016.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".