Diagnostic Delays in Inflammatory Bowel Disease
Bibliographic record
Abstract
Introduction: Canada has the highest reported incidence and prevalence of inflammatory bowel disease (IBD) in the world. IBD has significant impact on quality of life and also a substantial economic burden on health care system, amounting to $12,000 annually per patient with IBD. Earlier diagnoses of IBD and earlier therapeutic interventions have been associated with reduction in surgical and hospitalization rates in patients with IBD. A significant challenge in the diagnosis of IBD is its similarities with Irritable bowel syndrome (IBS), as patients present with similar symptoms including diarrhea and abdominal pain. The gold standard for distinguishing the two is a colonoscopy, which remains a scarce resource. Our objective is to determine the time to diagnosis of newly referred patients with suspected IBD. We evaluate new referrals with suspected IBD and determine the wait times both to be seen in the clinic and to undergo an endoscopic evaluation. Methods: We performed a retrospective chart review looking at new referrals to IBD clinic from January 2013 to December 2013. All new consultations were reviewed and data pertaining to patient demographic, disease onset, disease severity, referral, and triage process were recorded. Patients who have been seen previously in the IBD clinic and patients who failed to attend their appointment were excluded. Clinic wait time, time to endoscopy, diagnostic delay was determined. Results: In total 145 charts were reviewed. Out of these 62 patients had a previous diagnosis of IBD and 69 patients were undifferentiated. Looking at the latter group, 23 patients were diagnosed with IBS, 7 with Crohn’s disease, and 6 with ulcerative colitis. There were no significant differences in clinic wait times and endoscopy times for patients diagnosed with IBD vs. IBS (p=0.78, p=0.67 respectively). Diagnostic delays were significantly different for patients with IBD vs. IBS at 17 months and 60 months respectively (p=0.003). Conclusion: Comparable wait times and endoscopy times for patients with IBD and IBS are consistent with their clinical similarities. However, they also suggest the need for better triaging process and noninvasive diagnostic studies to risk stratify patients. We plan to study this further by closely looking at referrals, formulating a standardized referral form. In addition to this, we plan to study whether triage on basis of fecal calprotectin will have an effect on wait times and diagnostic delays.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".