Disease monitoring strategies in inflammatory bowel diseases: What do we mean by ‘tight control’?
Bibliographic record
Abstract
In recent years, there has been a critical change in treatment paradigms in inflammatory bowel diseases (IBD) triggered by the arrival of new effective treatments aiming to prevent disease progression, bowel damage and disability. The insufficiency of symptomatic disease control and the well-known discordance between symptoms and objective measures of disease activity lead to the need of reviewing conventional treatment algorithms and developing new concepts of optimal therapeutic strategy. The treat-to-target strategies, defined by the selecting therapeutic targets in inflammatory bowel disease consensus recommendation, move away from only symptomatic disease control and support targeting composite therapeutic endpoints (clinical and endoscopical remission) and timely assessment. Emerging data suggest that early therapy using a treat-to-target approach and an algorithmic therapy escalation using regular disease monitoring by clinical and biochemical markers (fecal calprotectin and C-reactive protein) leads to improved outcomes. This review aims to present the emerging strategies and supporting evidence in the current therapeutic paradigm of IBD including the concepts of "early intervention", "treat-to-target" and "tight control" strategies. We also discuss the real-word experience and applicability of these new strategies and give an overview on the future perspectives and areas in need of further research and potential improvement regarding treatment targets and ("tight") disease monitoring strategies.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| 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".