Therapeutic drug monitoring of biologics in inflammatory bowel disease: An evidence-based multidisciplinary guideline
Bibliographic record
Abstract
Therapeutic drug monitoring (TDM) has emerged as a valuable tool for optimizing the use of biologics in inflammatory bowel disease (IBD). However, variations in focus, methodology, and recommendations among relevant guidelines and consensuses have contributed to inconsistencies in their quality. This guideline synthesizes current evidence to standardize TDM of biologics in IBD, and improve patient outcomes. This multidisciplinary guideline was developed in collaboration with pharmacy, gastroenterology, and pharmacology associations in China. The guideline development group included 9 experts in clinical pharmacy, 4 experts in TDM, 8 gastroenterologists, and 2 methodologists. A comprehensive search was conducted across PubMed, Embase, Web of Science, the Cochrane Library databases, as well as key gastroenterology-relevant guideline websites. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach was utilized, and this guideline was registered on the Guideline International Network website. Internal and external reviews were conducted. We proposed 5 clinical questions under two overarching themes. Based on the current evidence and the clinical opinions of the core working group members, the initial recommendations were made. Following comprehensive internal and external review processes, 14 recommendations (1 strong and 13 weak) were finalized for the clinical questions. To our knowledge, this is the first evidence-based clinical practice guideline on TDM in patients with IBD developed using the GRADE approach. It addresses five key questions: whether TDM leads to better therapeutic outcomes than conventional treatment, what indicators should be monitored, when TDM should be initiated, what the therapeutic drug trough concentration thresholds are, and which TDM method (proactive or reactive) can better improve therapeutic outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".