Review: advice from doctors, counselling by nurses, behavioural interventions, nicotine replacement therapy, and several pharmacological treatments increase smoking cessation rates
Bibliographic record
Abstract
Lancaster T, Stead L, Silagy C , et al for the Cochrane Tobacco Addiction Review Group. Effectiveness of interventions to help people stop smoking: findings from the Cochrane Library. BMJ2000 Aug 5; 321 : 355 –8 [OpenUrl][1][FREE Full Text][2] QUESTION: Are smoking cessation interventions effective? Reviews were identified by searching the Cochrane Library . Reviews were selected if they included randomised controlled trials of interventions to reduce or prevent tobacco use that had ≥6 months of follow up with outcomes of sustained abstinence or point prevalence quit rates. Extracted data included interventions and outcomes. 20 systematic reviews were available in the Cochrane Library . 1 review (including 31 trials and >26 000 participants who smoked) examined simple advice given by doctors during routine care and showed that the intervention increased quit rates (weighted odds ratio [OR] 1.69, 95% CI 1.45 to 1.98). Another review of individual counselling given by nurses also showed increased quit rates. Behavioural interventions for smoking cessation, in the forms of individual … [1]: {openurl}?query=rft.jtitle%253DBMJ%26rft.stitle%253DBMJ%26rft.aulast%253DLancaster%26rft.auinit1%253DT.%26rft.volume%253D321%26rft.issue%253D7257%26rft.spage%253D355%26rft.epage%253D358%26rft.atitle%253DRegular%2Breview%253A%2BEffectiveness%2Bof%2Binterventions%2Bto%2Bhelp%2Bpeople%2Bstop%2Bsmoking%253A%2Bfindings%2Bfrom%2Bthe%2BCochrane%2BLibrary%26rft_id%253Dinfo%253Adoi%252F10.1136%252Fbmj.321.7257.355%26rft_id%253Dinfo%253Apmid%252F10926597%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/ijlink?linkType=FULL&journalCode=bmj&resid=321/7257/355&atom=%2Febnurs%2F4%2F1%2F13.atom
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".