Implementing changes in clinical practice to improve the management of Crohn's disease
Bibliographic record
Abstract
The introduction of anti-tumour necrosis factor therapies has provided highly effective treatments for Crohn's disease, making it possible to significantly improve the prognosis of patients. However, neither conventional non-biological therapies nor anti-tumour necrosis factor therapies are routinely used to optimum effect. There are several reasons for this, including a lack of specific evidence to guide common clinical questions and a lack of clearly defined treatment targets. This paper suggests some simple changes to the management of Crohn's disease that have the potential to significantly improve patient outcomes. A new treatment target, 'deep remission', which includes mucosal healing as well as clinical remission, may be the first step in defining the successful treatment of Crohn's disease; early clinical studies have demonstrated that this is a readily achievable target. Initiating appropriate treatment early can increase clinical remission rates, improve steroid sparing, induce mucosal healing and prevent structural bowel damage, whereby reducing the need for hospitalization and surgery. There are also clear indications that modifying treatment based on regular objective assessments of disease activity to provide tight disease control can improve patient outcomes in a similar way to that observed in rheumatoid arthritis. These simple changes to management strategy appear to allow the full potential of available treatments to be realized. Clinical studies to further define optimized treatment strategies for Crohn's disease are underway and will provide future direction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".