Smoking May Reduce the Effectiveness of Anti-TNF Therapies to Induce Clinical Response and Remission in Crohn’s Disease: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Cigarette smoking worsens prognosis of Crohn's disease [CD]. We conducted a systematic review and meta-analysis to examine the association between smoking and induction of clinical response or remission with anti-tumour necrosis factor [TNF] therapy. METHODS: MEDLINE, EMBASE, PubMed, and Cochrane CENTRAL [June 2019] were searched for studies reporting the effect of smoking on short-term clinical response and remission to anti-TNF therapy [≤16 weeks following the first treatment] in patients with CD. Risk ratios [RR] with 95% confidence intervals [CI] were calculated using random-effects models. RESULTS: Eighteen observational studies and three randomised controlled trials [RCT] were included. Current smokers and non-smokers [never or former] had similar rates of clinical response [observational studies RR: 0.96; 95% CI: 0.88, 1.05; RCTs RR: 1.09; 95% CI: 0.84, 1.41]. When restricted to studies clearly defining the smoking exposure, smokers treated with anti-TNF were less likely to achieve clinical response than non-smokers [smokers defined as having ≥5 cigarettes/day for ≥6 months RR: 0.63; 95% CI: 0.48, 0.83; lifetime never smokers vs ever smokers excluding former smokers RR: 0.81; 95% CI: 0.71, 0.93]. Current smokers were also less likely to achieve clinical remission in observational studies [RR: 0.75; 95% CI: 0.57, 0.98], though this association was not seen in RCTs [RR: 1.04; 95% CI: 0.89, 1.21]. CONCLUSIONS: Smoking is significantly associated with a reduction in the ability of infliximab or adalimumab to induce short-term clinical response and remission when pooling studies where smoking status was clearly defined. When patients with CD are treated with highly effective therapy, including anti-TNF agents, concurrent smoking cessation may improve clinical outcomes.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".