Smoking did not modify the effects of anti-TNF treatment on health-related quality of life among Australian ankylosing spondylitis patients
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the impact of smoking on health-related quality of life (HRQoL) among AS patients who were taking biologic DMARDS. METHODS: This is a longitudinal cohort study of AS patients with anti-TNF treatment in the Australian Rheumatology Association Database (2003-11). They were assessed using the 36-item Short Form Health Survey (SF-36), Assessment of Quality of Life (AQoL) and HAQ for spondylitis (HAQ-S) on a biannual basis. Linear mixed models were used to assess the impact of smoking on HRQoL outcomes over the first 2 years of treatment. RESULTS: Four hundred and twenty-two patients [73% male, mean age 44.9 years (s.d. 12.7) provided 1189 assessments for the study. Current smokers (n = 79) were slightly younger, more likely to be male, less likely to use or to have previously used prednisolone and had a slightly shorter disease duration than past smokers (n = 138) or non-smokers (n = 205). After adjusting for smoking, gender, age, education, employment, co-morbidities and medication use, including DMARDs, anti-inflammatories and analgesics, all the HRQoL measures improved significantly over the study period and the improvements were not modified by smoking status (all P-values >0.36). Current smokers tended to have a poorer HRQoL on the SF-36 physical score [-1.93 (95% CI -3.94, 0.09), P = 0.06] and the HAQ-S score [0.10 (95% CI -0.01, 0.20), P = 0.07] compared with non-smokers. CONCLUSION: Among AS patients, active smoking did not diminish or modify the improvements in HRQoL from anti-TNF treatment, even though current smokers compared with non-smokers tended to have poorer scores in some HRQoL measures.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".