Smoking — Does It Affect Rheumatoid Arthritis Activity? Does It Matter?
Bibliographic record
Abstract
For decades it has been common knowledge that there is a connection between rheumatoid factor–positive and antibodies against citrullinated peptide–positive rheumatoid arthritis (RA) and smoking1. Cigarette smoking is regarded as one of the major environmental factors suggested to play a critical role in the development of a variety of disorders including autoimmune diseases such as RA, systemic lupus erythematosus, systemic sclerosis, multiple sclerosis, and Crohn’s disease2. It is safe to say that there is a widespread appreciation of the odiousness of smoking. However, does smoking also have an effect on actual disease activity and on the prognosis of patient outcome? Some statistical procedures, as well as some people, may nourish the illusion that there really is such a thing as a one-and-only risk factor. However, is this the case in reality? Is life really that simple? Many aspects should be considered in relation to smoking and its consequences for patients with RA. Cigarette smoke represents a mixture of more than 4000 toxic substances including nicotine, polycyclic aromatic hydrocarbons, organic compounds, solvents, gas substances (primarily carbon monoxide), and free radicals. Extensive data suggest that smoking has a modulator role in the immune system contributing to a shift from Th type 1 to Th type 2 immune response3. Although there is consensus that smoking is highly likely to play a role in the pathogenesis of RA, does it also influence disease activity? The respective data … Address correspondence to Dr. B.F. Leeb, 2nd Dept. of Medicine, Center for Rheumatology, Lower Austria, Landstrasse 18, Karl Landsteiner Institute for Clinical Rheumatology, Stockerau, A-2000, Austria. E-mail: burkhard.leeb{at}stockerau.lknoe.at
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".