A222 EFFECT OF CLINICAL FACTORS ON IBD TREATMENT RESPONSE: FINDINGS FROM A NOVEL SINGLE CENTER PATIENT REGISTRY
Bibliographic record
Abstract
Abstract Background Inflammatory bowel disease (IBD), comprising both Crohn’s disease as well as ulcerative colitis, has shown heterogenous response to therapy. Over the past decades, targeted biologic therapies have become the mainstay of treatment. Even with the rapid pace of progress in this field, roughly a third of patients do not show an initial response to these treatments. The ability to accurately predict treatment response to therapy would both improve patient safety and satisfaction as well as decrease costs from ineffective therapies. Purpose The primary aim of this project is to identify clinical factors predictive of disease response to induction biologic therapy in inflammatory bowel disease through the creation of a novel single center patient registry. Method Initially, a single center tissue biopsy registry of IBD patients receiving colonoscopy was created. Retrospective clinical data was subsequently collected on patients identified to have initiated or changed biologic therapy after entry into the registry. Clinical data regarding age, sex, comorbidities, lifestyle factors, time since initial diagnosis, extra intestinal manifestations, serum markers, prior IBD treatments, planned treatment, clinical response, as well as endoscopic response were collected. A Pilot study of the first 30 patients identified was undertaken. Individual clinical factors were compared between patients with a documented clinical response to biologic therapy and non-responders at the group level as well as by biologic therapy initiated. Result(s) No statistically significant differences in collected clinical data parameters were observed between treatment responders and non responders in aggregate as well as when sub selected by individual biologic therapy initiated, bio-naive status, and steroid dependence. Conclusion(s) This project has demonstrated that clinical factors alone do not adequately predict disease response in inflammatory bowel disease. Future work will expand this data registry to further clinical factors and continue patient enrolment. Next steps also include the addition of tissue level data, in particular tissue level RNA data. It is hoped that these parameters, either individually or in combination, will provide a robust and accessible predictor of response to biologic therapy in inflammatory bowel disease. Please acknowledge all funding agencies by checking the applicable boxes below Other Please indicate your source of funding below: Western University Department of Medicine Disclosure of Interest None Declared
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".