P246 Development of a new Inflammatory Bowel Disease Patient Identifier shortens time to clinic review and initiation of treatment
Bibliographic record
Abstract
Abstract Background Timely diagnosis of inflammatory bowel disease (IBD) is important because earlier use of biologic therapies leads to mucosal healing, a reduction in hospitalisations and surgeries and improvements in quality of life. In the United Kingdom, general practitioners refer patients with symptoms suggestive of IBD to colorectal surgeons, emergency department physicians, gastroenterologists or directly for a lower gastrointestinal endoscopy. Consequently, there is variation in the time from endoscopic diagnosis of IBD to specialist review. We created an electronic tool that screens all endoscopy reports for a new diagnosis of IBD. These cases are then reviewed in our weekly complex multidisciplinary team (MDT) meeting and new patients are allocated an IBD specialist. We sought to determine whether our new patient identifier reduced the time to clinical review by an IBD specialist and initiation of IBD treatment. Methods We designed a retrospective observational cohort study comparing time from endoscopic diagnosis of IBD to initiation of treatment and IBD specialist review. Outcomes were compared in the 18 months before and after the introduction, in January 2018, of our new IBD patient identifier. Demographic, endoscopic and treatment outcome data were recorded from our electronic patient record. Categorical and continuous variables were summarised as frequencies (%) and median [IQR] and compared with Fisher’s exact and Mann Whitney U tests respectively. Results Between the 1st January 2018 and 30th June 2019, 116 new patients diagnosed with IBD were identified and reviewed in our MDT meeting. In the preceding 18 months, 111 patients were diagnosed with IBD. There were no significant differences between groups according to sex, age, age at diagnosis, disease type, or phenotype according to the Montreal classification (Table 1). Both the median time from endoscopic diagnosis to treatment initiation (0 days [IQR 0 – 14.2] vs 14 days [IQR 0 – 31.6], p=0.007) and specialist clinic review (21 days [IQR 7 - 25] vs 48 days [IQR 34 – 63], p<0.0001) were shorter following the introduction of the new IBD patient identifier screening tool (Figure 1). Conclusion Systematic electronic screening of endoscopic reports linked to MDT review reduces the time to first treatment and specialist review in newly diagnosed patients with 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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".