Predictability of biomarkers and disease activity indices for efficacy of advanced therapies in Crohn’s disease
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is a condition that causes long-term inflammation in the digestive system, also known as the gastrointestinal (GI) tract, with symptoms that may include diarrhea, abdominal pain, and rectal bleeding. This condition affects more than 6.8 million people worldwide. There are two main types of IBD: ulcerative colitis (UC) and Crohn’s disease (CD). UC causes inflammation and sores (ulcers) in the lining of the lower part of the GI tract, whereas CD can affect any part of the GI tract and multiple layers of the intestinal wall. To monitor and evaluate the severity of CD, doctors rely on a combination of patient-reported symptoms, blood tests, and medical procedures. Based on clinical guidelines, these tools help determine how well a patient responds to treatment or whether they achieve remission (a period when symptoms improve significantly or disappear). When researchers assess improvements in CD, they use specific measurement tools, or "indices," to track changes in patients’ conditions. These indices can focus on clinical symptoms (such as pain or diarrhea), endoscopic results (where a small camera has been used to view the GI tract) or blood tests. However, there is no clear agreement on the best way to measure these improvements, which creates challenges in determining whether new treatments are effective. For example, there is debate about which thresholds of improvement in these indices indicate meaningful changes for patients. Additionally, some methods may not be sensitive enough to capture improvements in patients with milder symptoms. Our research aims to address these challenges by studying the strengths and limitations of the available indices used to measure treatment response in CD. We will also explore how well these indices capture improvements in patients with varying levels of disease severity. This research is important because clearer and more standardized measures of improvement can help ensure that new treatments for CD are accurately evaluated and compared. Ultimately, this could lead to better care and outcomes for people living with CD. To conduct this research, we will review existing studies and data on CD measurement tools to analyze their effectiveness, reliability, and ability to detect clinically meaningful improvements. By identifying the most appropriate and reliable measures, we hope to contribute to more precise and patient-centered approaches to treating CD.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".