IDDF2019-ABS-0130 Global smoking trends in inflammatory bowel disease: a systematic review of inception cohorts
Bibliographic record
Abstract
Background The effect of smoking on inflammatory bowel diseases (IBD) may be heterogeneous across ethnicity and geography. This is a systematic review of smoking prevalence across global IBD cohorts. Methods A systematic literature search was conducted on Medline and Embase from January 1st, 1946 to April 5th, 2018 to identify population-based studies assessing the prevalence of smoking at diagnosis in inception cohorts of Crohn’s disease (CD) or ulcerative colitis (UC). Studies that did not report smoking data from the time of diagnosis or the year of IBD diagnosis were excluded. Prevalence of smoking in IBD was stratified by geography and across time. Results We identified 56 studies that were eligible for inclusion. Never-smokers in the newly diagnosed CD population in the West has increased over the last two decades, especially in the United Kingdom and Sweden; +26.6% and +11.2% respectively. Never-smokers at CD diagnosis in newly industrialised nations have decreased over the 1990s and 2000s; China (-19.36%). Never-smokers at UC diagnosis also decreased in China; -15.4%. The former-smoker population at UC diagnosis in China is expanding; 11%(1990–2006) to 34%(2011–2013). Conclusions There has been a reduction in the prevalence of smoking in the IBD cohort in the West. This is not consistent globally. Although smoking prevalence has decreased in the general population of newly industrialised nations, this remains an important risk factor with longer-term outcomes awaiting translation in both UC and CD.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.002 |
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".