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Allen Carr’s Easyway to Stop Smoking - A randomised clinical trial

2018· article· en· W2898523926 on OpenAlexfundno aff
Sheila Keogan, Shasha Li, Luke Clancy

Bibliographic record

VenueTobacco Control · 2018
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersTerry Fox Research InstituteTechnological University DublinPfizer
KeywordsMedicineRandomized controlled trialDemographySmoking cessationPopulationInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.066
GPT teacher head0.386
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2018
Admission routes1
Has abstractyes

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