Treating beyond symptoms with a view to improving patient outcomes in inflammatory bowel diseases
Bibliographic record
Abstract
BACKGROUND AND AIMS: Treatment goals in inflammatory bowel diseases are evolving beyond the control of symptoms towards the tight control of objectively-measured gastrointestinal inflammation. This review discusses the progress and challenges in adopting a treat-to-target approach in inflammatory bowel diseases. METHODS: Evidence from the literature that highlights current thinking in terms of treating-to-target in patients with inflammatory bowel diseases is discussed. RESULTS: Monitoring for objective evidence of inflammation using endoscopy, cross-sectional imaging or laboratory biomarkers may be a useful approach in inflammatory bowel diseases; however, setting the appropriate treatment goal remains a challenge. Deep remission (a composite of symptom control and mucosal healing) may now be a realistic target in Crohn's disease; however, it remains to be proven that achieving deep remission will modify the long-term disease course. Assessing prognosis at an early stage of the disease course is essential for the development of an appropriate management plan, with the rationale of adapting treatment to disease severity. An algorithm has been proposed for the treatment of early Crohn's disease that involves early treatment with immunosuppressants and tumour necrosis factor antagonists, in the hope of preventing structural bowel damage. CONCLUSIONS: Treating beyond symptoms will require a clear management plan influenced by disease severity at presentation, clinical and biological prognostic factors, achievement and maintenance of clinical and biological remission and pharmacoeconomics.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".