Outcomes and Strategies to Support a Treat-to-target Approach in Inflammatory Bowel Disease: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND AIMS: Management of Crohn's disease and ulcerative colitis has typically relied upon treatment intensification driven by symptoms alone. However, a 'treat-to-target' management approach may help to address underlying inflammation, minimise disease activity at early stages of inflammatory bowel disease, limit progression, and improve long-term outcomes. METHODS: A systematic literature review was conducted to identify data relevant to a treat-to-target approach in inflammatory bowel disease, published between January 1, 2007 and May 15, 2017. RESULTS: Consistent with recommendations of the Selecting Therapeutic Targets in Inflammatory Bowel Disease [STRIDE] working group, studies have investigated factors influencing the achievement of both endoscopic and histological mucosal healing and patient-level outcomes in inflammatory bowel disease [IBD]. Histological healing and biomarker levels have also been shown to be modifiable outcomes. Although there is a lack of prospectively derived evidence validating mucosal healing as a treatment target, data are emerging to suggest that targeting mucosal healing or inflammation rather than symptoms may be cost-effective in some settings. The review highlighted several strategies that may support the implementation of a treat-to-target approach in IBD. The prospective randomised CALM study demonstrated how tight control [whereby treatment decisions are based on close monitoring of inflammatory biomarkers] leads to improvements in endoscopic and clinical outcomes. The review also considered the influence of coordinated care from a multidisciplinary team and patient engagement with improved adherence, as well as the role of therapeutic drug monitoring in inflammatory bowel disease management. CONCLUSIONS: A treat-to-target strategy may impact on disease progression and improve outcomes in inflammatory bowel disease. Prospective studies including long-term data are required to ensure that the most appropriate targets and strategies are identified.
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 | 0.016 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".