S935 Optimizing Access for Individuals With Suspected Inflammatory Bowel Disease Through the Development of a Triage Tool
Bibliographic record
Abstract
Introduction: The negative impact of a delayed inflammatory bowel disease (IBD) diagnosis has been well established. We created a clinical pathway referred to as the “High-Risk IBD clinic” within a centralized referral program in a tertiary referral centre, in order to improve access to subspecialist care for individuals suspected but not yet diagnosed with IBD. Information required included symptoms (e.g. diarrhea, abdominal pain, rectal bleeding), risk factors (e.g. family history, rheumatological disease) and investigations (e.g. hemoglobin, CRP, abdominal imaging). The current study evaluated the effectiveness of this pathway in comparison to recommended benchmarks and created predictive models to identify factors associated with an IBD diagnosis. Methods: We conducted a retrospective cohort study of referrals to the High-Risk IBD clinic from February 2014 until December 2018. Referral information, investigations, endoscopic findings and final diagnosis were obtained from 316 consented individuals. Univariate logistic regression was performed to explore the association between factors included in the referral form, and a diagnosis of Crohn’s disease (CD) and ulcerative colitis (UC). For creation of predictive models, any variable with a P-value of < 0.1 in univariate logistic regression was selected for entry into the multivariate model for CD and UC. Results: Individuals presenting with high-risk features for IBD waited a median of 69 days (Q1-Q3: 47, 102 days) before initial consultation and a median of 73 days for initial endoscopy (Q1-Q3: 51, 106 days), which remains above the recommended benchmarks. Data obtained from referrals to the High-Risk IBD clinic were used to create prediction models. For UC, the predictive model included weight loss (OR 3.13, P = 0.030), presence of rectal bleeding (OR 5.62, P = 0.009) and abdominal pain (OR 0.33, P = 0.032). For CD, the predictive model included male gender (OR 3.35, P = 0.003), elevated CRP (OR 2.30, P = 0.043) and weight loss (OR 2.26, P = 0.040). Conclusion: Timely access to care for individuals with IBD continues to be a barrier. We established predictive tools associated with a final diagnosis of IBD and IBS as a means to expedite the care of individuals with undiagnosed IBD.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".