Optimising monitoring in the management of Crohn's disease: A physician's perspective
Bibliographic record
Abstract
Management of Crohn's disease has traditionally placed high value on subjective symptom assessment; however, it is increasingly appreciated that patient symptoms and objective parameters of inflammation can be disconnected. Therefore, strategies that objectively monitor inflammatory activity should be utilised throughout the disease course to optimise patient management. Initially, a thorough assessment of the severity, location and extent of disease is needed to ensure a correct diagnosis, identify any complications, help assess prognosis and select appropriate therapy. During follow-up, clinical decision-making should be driven by disease activity monitoring, with the aim of optimising treatment for tight disease control. However, few data exist to guide the choice of monitoring tools and the frequency of their use. Furthermore, adaption of monitoring strategies for symptomatic, asymptomatic and post-operative patients has not been well defined. The Annual excHangE on the ADvances in Inflammatory Bowel Disease (IBD Ahead) 2011 educational programme, which included approximately 600 gastroenterologists from 36 countries, has developed practice recommendations for the optimal monitoring of Crohn's disease based on evidence and/or expert opinion. These recommendations address the need to incorporate different modalities of disease assessment (symptom and endoscopic assessment, measurement of biomarkers of inflammatory activity and cross-sectional imaging) into robust monitoring. Furthermore, the importance of measuring and recording parameters in a standardised fashion to enable longitudinal evaluation of disease activity is highlighted.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".