New tools and approaches for improved management of inflammatory bowel diseases
Bibliographic record
Abstract
BACKGROUND AND AIMS: Inflammatory bowel diseases are part of a wider conglomeration of immune-mediated inflammatory diseases. New management approaches need to be developed as we understand more of the epidemiology and aetiology of inflammatory bowel diseases and medical care becomes more complex. METHODS: Selected new tools and approaches for improved management of inflammatory bowel diseases are presented, based on published evidence and clinical experience. RESULTS: Setting quality of care standards that are consistent across different inflammatory bowel disease care settings will be paramount and require collaboration between specialist and non-specialist centres. Alongside this, the value of care will need to be evaluated in terms of maximising outcomes over the entire care cycle for a patient. In moving towards a value-based approach to management, it is important to determine the progression rate of the disease by measuring cumulative bowel damage. As well as understanding the course of disease in individual patients, it is also becoming more feasible to individualise therapy and exploit drug pharmacology to achieve better and more long-term responses. Finally, it is timely to consider formal collaborations between specialists in immune-mediated inflammatory diseases to ensure more cohesive patient care. CONCLUSIONS: The potential for improved management of patients with inflammatory bowel diseases continues to increase as we look to understand when and how to intervene in the disease process and how to adopt a collaborative management approach that promotes networking and reduces heterogeneity of care across different care settings.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".