Factors Associated with Delayed Diagnosis of Crohn's Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract BACKGROUND Delayed diagnosis is a major barrier to the effective management of Crohn's disease (CD). Several studies have investigated factors responsible for delays in diagnosis, but no meta-analyses have systematically assessed the impact of these factors. AIM To assess the impact of various factors on the delayed diagnosis of CD. METHODS PubMed, EMBASE, and Web of Science databases were searched to identify observational studies published before April 2022 that assessed factors associated with delays in CD diagnosis. We excluded review articles, case reports, or commentaries without original data.We pooled effect sizes as odds ratios (OR) using random effects models for each risk factor examined in at least three distinct samples. We assessed study quality on the basis of the Newcastle-Ottawa Scale and examined between-study heterogeneity. A funnel plot was used for visual assessment of publication bias. The study protocol was registered with PROSPERO, CRD42022322251. RESULTS A total of 18 studies were included in the paper, covering 13 countries. The study sample consisted of 9,669 cases. Ileal CD (OR =1.46, 95% CI =1.21–1.76), smoking at the time of diagnosis (OR =1.19, 95% CI =1.02–1.38), and use of NSAIDs (OR =1.34, 95% CI =1.04–1.72) were significantly associated with a delay in CD diagnosis. CONCLUSION The findings suggest that ileal CD, use of NSAIDs, and smoking are risk factors for the delayed diagnosis of CD. Education of patients and primary care providers about these risk factors should be increased.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".