A nurse managed smoking cessation and relapse prevention programme did not reduce smoking rates at 12 months beyond rates achieved by usual care in women with cardiovascular disease
Bibliographic record
Abstract
Sivarajan Froelicher ES, Miller NH, Christopherson DJ, et al . High rates of sustained smoking cessation in women hospitalized with cardiovascular disease: the Women’s Initiative for Nonsmoking (WINS). Circulation 2004;109:587–93.[OpenUrl][1][Abstract/FREE Full Text][2] Q Is a nurse managed, cognitive behavioural, smoking cessation and relapse prevention programme effective for women admitted to hospital with cardiovascular disease (CVD)? ### ![Graphic][3]</img>Design: randomised controlled trial. ### ![Graphic][4]</img>Allocation: {concealed}*. ### ![Graphic][5]</img>Blinding: blinded (data collectors and outcome assessors). ### ![Graphic][6]</img>Follow up period: 30 months. ### ![Graphic][7]</img>Setting: 10 hospitals in the San Francisco Bay area, California, USA. ### ![Graphic][8]</img>Patients: 277 women ⩾18 years of age (mean age 61 y) who were admitted to hospital with CVD or peripheral vascular disease, had smoked cigarettes in the month before admission, and were willing to make a serious attempt to quit smoking after discharge. Exclusion criteria included medical instability, alcohol or substance abuse, dementia, and schizophrenia. ### ![Graphic][9]</img>Intervention: smoking cessation and relapse prevention intervention (brief physician counselling and usual care … [1]: {openurl}?query=rft.jtitle%253DCirculation%26rft.stitle%253DCirculation%26rft.aulast%253DSivarajan%2BFroelicher%26rft.auinit1%253DE.%2BS.%26rft.volume%253D109%26rft.issue%253D5%26rft.spage%253D587%26rft.epage%253D593%26rft.atitle%253DHigh%2BRates%2Bof%2BSustained%2BSmoking%2BCessation%2Bin%2BWomen%2BHospitalized%2BWith%2BCardiovascular%2BDisease%253A%2BThe%2BWomen%2527s%2BInitiative%2Bfor%2BNonsmoking%2B%2528WINS%2529%26rft_id%253Dinfo%253Adoi%252F10.1161%252F01.CIR.0000115310.36419.9E%26rft_id%253Dinfo%253Apmid%252F14769679%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=ABST&journalCode=circulationaha&resid=109/5/587&atom=%2Febnurs%2F7%2F3%2F83.atom [3]: /embed/inline-graphic-1.gif [4]: /embed/inline-graphic-2.gif [5]: /embed/inline-graphic-3.gif [6]: /embed/inline-graphic-4.gif [7]: /embed/inline-graphic-5.gif [8]: /embed/inline-graphic-6.gif [9]: /embed/inline-graphic-7.gif
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".