Systematic review with meta‐analysis: association between acetaminophen and nonsteroidal anti‐inflammatory drugs (<scp>NSAID</scp>s) and risk of Crohn's disease and ulcerative colitis exacerbation
Bibliographic record
Abstract
Summary Background Unlike acetaminophen, nonsteroidal anti‐inflammatory drugs (NSAIDs) have generally been thought to be associated with increased risk of IBD exacerbation. Aim To carry out a systematic review and meta‐analysis of previous studies examining the association between acetaminophen and NSAIDs including cyclooxygenase (COX‐2) inhibitors use, and risk of Crohn's disease (CD) and ulcerative colitis (UC) exacerbation. Methods We identified published manuscripts and abstracts through 1 March 2017 by systematic search of Medline, Embase, Cochrane and other trial registries. Quality assessment was done using Newcastle‐Ottawa scale and random‐effect meta‐analysis using pooled relative risks (RRs) and 95% CIs were calculated. Results Eighteen publications between years 1983 and 2016 were identified. For the meta‐analysis, pooled RRs of disease exacerbation with NSAIDs use were (1.42, 95% CI, 0.65‐3.09), I 2 = 60.3% for CD, and (1.52, 95% CI, 0.87‐2.63), I 2 = 56.1% for UC. The corresponding values for acetaminophen use were (1.40, 95% CI, 0.96‐2.04), I 2 = 45.6% for UC, and (1.56, 95% CI, 1.22‐1.99), I 2 = 0.0% for IBD. Sensitivity analyses limited to studies with low risk of bias showed a significantly increased risk of CD exacerbation (1.53, 95% CI, 1.08‐2.16) but not UC (0.94, 95% CI, 0.36‐2.42) with NSAIDs use. Conclusions Contrary to generally accepted belief, we did not find a consistent association between NSAIDs use and risk of CD and UC exacerbation. There was also no consistent evidence for association with acetaminophen although further studies are needed.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.047 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".