How generalisable to community samples are clinical trial results for treatment of nicotine dependence: a comparison of common eligibility criteria with respondents of a large representative general population survey
Bibliographic record
Abstract
OBJECTIVE: To examine the generalisability of findings from clinical trials of individuals with nicotine dependence to a large general population sample. METHODS: Eligibility criteria were drawn from typical criteria of clinical trials for nicotine dependence. The National Epidemiological Survey on Alcohol and Related Conditions (NESARC), a large national sample of the US population, was used to assess how many potentially eligible people would fulfil the eligibility criteria. NESARC interviewed more than 43,000 adults aged 18 years and older. We applied a standard set of eligibility criteria representative of smoking cessation clinical trials to all the 4962 adults with nicotine dependence in the past 12 months, and then to a subgroup of participants motivated to quit (n=4121). RESULTS: We found that approximately six out of 10 participants (65.89%) with nicotine dependence were excluded by at least one criterion. In the subgroup of nicotine-dependent participants motivated to quit, more than half (58.60%) were excluded by at least one criterion. For the overall sample, smoking 10 cigarettes per day or less and lack of motivation to quit were the two criteria leading to exclusion for the greatest percentage of individuals (32.02% and 17.60%, respectively). For the sample motivated to quit, smoking 10 cigarettes or fewer per day and current depression led most frequently to exclusion (33.79% and 15.71%, respectively). CONCLUSIONS: Further studies and interventions should explore the efficacy of tobacco treatment interventions in a larger segment of the population, notably in the subpopulations of people with nicotine dependence who smoke fewer than 10 cigarettes per day or who have comorbid depression.
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 | 0.688 | 0.904 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".