The impact of smoking on prevalence of psoriasis and psoriatic arthritis
Bibliographic record
Abstract
OBJECTIVES: In this systematic literature review and meta-analysis, we aimed to investigate the impact of cigarette smoking on the prevalence and incidence of psoriasis and psoriatic arthritis (PsA). METHOD: We performed a systematic literature review using the MEDLINE, EMBASE and Cochrane Central Register databases. The literature included publications from January 1980 to July 2019. The studies that provided clear information on the number of patients with ever smoking data were included in the meta-analysis. RESULTS: The systematic literature review identified 52 and 24 articles for the prevalence of smoking in psoriasis and PsA, respectively. Of these, 16 articles on psoriasis and three and four (general population and psoriasis, respectively) articles on PsA met the criteria and were included in the meta-analysis. The prevalence of ever smoking was increased in psoriasis compared with the general population (OR: 1.84; 95% CI: 1.4, 2.3). For PsA the prevalence of ever smoking was reduced in psoriasis patients (OR: 0.70; 95% CI: 0.60, 0.81), but not changed compared with the general population (OR: 1.10; 95% CI: 0.92, 1.32). CONCLUSION: This meta-analysis showed that ever smoking increases the risk of psoriasis in the general population, but may reduce the risk of PsA in psoriasis patients. The latter may be also due to the collider effect. Whether smoking cessation neutralizes the risk of developing psoriasis requires a well-defined smoking data collection for the past history and this is currently unavailable in the literature.
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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 | 0.026 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.039 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".