Relationship Between Pack-year History of Smoking and Response to Tumor Necrosis Factor Antagonists in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine whether there is a quantitative relationship between smoking history and response to therapy with tumor necrosis factor (TNF) antagonists. METHODS: A history of cigarette smoking was obtained from a questionnaire completed by each patient starting therapy with TNF antagonists since 2002 (n=154). A core set of demographic and clinical variables was recorded at baseline and at 3 and 12 months. The extent of smoking was quantified in pack-years (py), with 1 py equivalent to 20 cigarettes per day for 1 year. The association between smoking intensity and response was assessed using contingency tables and logistic regression analysis. Response to therapy was defined according to the European League Against Rheumatism improvement criteria. RESULTS: There was an increasing trend of no response at 3 and 12 months with increasing py history [p (trend)=0.008 and 0.003, respectively]. The change in Disease Activity Score (DAS)28 over the first 3 months was inversely associated with the number of py (r=-0.28, p=0.002). The association of py history with response failure was independent of age, sex, disease duration, baseline disease activity score (DAS28), Health Assessment Questionnaire (HAQ) score, IgM rheumatoid factor, and smoking at baseline. The most significant effect was seen in patients treated with infliximab. CONCLUSION: RA patients with a history of smoking were more likely to show a poor response to TNF antagonists. Response failure was associated with the intensity of previous smoking, irrespective of smoking status at initiation of anti-TNF therapy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".