Smoking cessation advice by rheumatologists: results of an international survey
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to understand practices regarding smoking cessation among rheumatologists for patients with inflammatory rheumatic diseases. METHODS: A survey was sent to the rheumatologists participating in the multinational Quantitative Standard Monitoring of Patients with Rheumatoid Arthritis (QUEST-RA) group. The survey inquired about the clinical practice characteristics and practices regarding smoking cessation (proportion of smokers with inflammatory rheumatic diseases given smoking cessation advice, specific protocols and written advice material, availability of dedicated smoking cessation clinic). RESULTS: Rheumatologists from 44 departments in 25 countries (16 European) completed the survey. The survey involved 395 rheumatologists, of whom 25 (6.3%) were smokers, and 199 nurses for patient education, of whom 44 (22.1%) were smokers. Eight departments (18.1 %) had a specific protocol for smoking cessation; 255 (64.5%) rheumatologists reported giving smoking cessation advice to all or almost all smokers with inflammatory diseases. In a regression model, early arthritis clinics (P = 0.01) and high gross domestic product countries (P = 0.001) were both independently associated with advice by the rheumatologist. Nurse gives advice to most patients in 11 of the 36 (30.5%) departments with nurses for patient education. CONCLUSION: Advice for smoking cessation within rheumatology departments is not homogeneous. In half of the departments, most doctors give advice to quit smoking to all or almost all patients with inflammatory diseases. However, only one in five departments have a specific protocol for smoking cessation. Our data highlight the need to improve awareness of the importance of and better practice implementation of smoking cessation advice for inflammatory rheumatic disease patients.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".