A157 PREDICTING SURVIVAL TIME FOR BOWEL RESECTION IN INDIVIDUAL CROHN DISEASE PATIENTS
Bibliographic record
Abstract
Abstract Background Pediatric onset Crohn disease (pCD) tends to be more complicated with a higher likelihood of requiring intestinal resection surgery. Currently there are no predictive tools for surgery in pCD; risk scores and relative survival analysis are available, but these cannot accurately calculate the probability of surgery in children with CD at an individual level. Aims This study aims to apply machine learning to create an individual survival time distributions (ISD) tool for accurate prediction of bowel resection over time in a novel pCD patient. Methods A prospectively-followed cohort of pCD patients (n= 934) was collected through the Canadian Children Inflammatory Bowel Disease Network (CIDsCaNN) inception cohort (2013-2021). 58 of the 934 pCD patients underwent surgery, while the remaining 876 either did not require surgery by the study’s end, transitioned to adult care, or were lost to follow-up. Surgeries included bowel resections for stricturing or penetrating disease. Clinical, laboratory, and treatment data were compiled into three datasets: baseline, induction (baseline + 10 weeks follow-up data), and longitudinal (baseline + 3 equally spaced follow-up visits with all treatment data). To develop the ISD predictor, we built a learning algorithm that includes maximum relevance minimum redundancy (mRMR) feature selection, Cox Proportional Hazards, and Random Survival Forest models. To estimate the quality of the ISD predictor, measured by the Integrated Brier Score (IBS) and Concordance Index (C-Index), we used k-fold (external) cross validation. Results Our resulted ISD predictors, two trained Cox Proportional Hazards models and one Random Survival Forest model, had IBS scores < 0.1 and C-Index scores > 0.83. These results indicate that our models provide a reliable ranking of survival times based on the individual probability for surgery. The results also showed that the features that are most informative for prediction of surgery were stricturing behavior, penetrating behavior, and distal bowel involvement. Conclusions This study suggests that it is possible to produce an ISD predictor capable of forecasting bowel resection over future times in a novel pCD patient. This novel approach can build tools to improve both the choice and timing of therapy. Funding Agencies CIHRWomen and Children’s Health Research Institute
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".