Smoking, Smoking Cessation, and Disease Activity in a Large Cohort of Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: While cigarette smoking is the best-studied environmental factor contributing to rheumatoid arthritis (RA), no study to date has examined the influence of smoking cessation on disease activity. We examined this relationship in an observational cohort of patients with RA in the United States. METHODS: Patients enrolled in the Consortium of Rheumatology Researchers of North America registry (CORRONA) were stratified into never, former, and current smokers at enrollment. Current smokers were further stratified into continued and ceased smoking groups during their followup in the registry. The primary outcome was change in Clinical Disease Activity Index (CDAI) at last visit in a multivariate, random-effects regression model accounting for multiple timepoints. RESULTS: At last visit, there was no significant change in CDAI between ceased smokers and continued smokers (coefficient -0.00091, SE 0.0033, p = 0.7834). The study did confirm prior cross-sectional studies that current smokers have worse disease activity than former or never smokers. CONCLUSION: In the short term, smoking cessation did not appear to influence change in disease activity over time.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".