Review article: consensus statements on therapeutic drug monitoring of anti‐tumour necrosis factor therapy in inflammatory bowel diseases
Bibliographic record
Abstract
BACKGROUND: Therapeutic drug monitoring (TDM) in inflammatory bowel disease (IBD) patients receiving anti-tumour necrosis factor (TNF) agents can help optimise outcomes. Consensus statements based on current evidence will help the development of treatment guidelines. AIM: To develop evidence-based consensus statements for TDM-guided anti-TNF therapy in IBD. METHODS: A committee of 25 Australian and international experts was assembled. The initial draft statements were produced following a systematic literature search. A modified Delphi technique was used with 3 iterations. Statements were modified according to anonymous voting and feedback at each iteration. Statements with 80% agreement without or with minor reservation were accepted. RESULTS: 22/24 statements met criteria for consensus. For anti-TNF agents, TDM should be performed upon treatment failure, following successful induction, when contemplating a drug holiday and periodically in clinical remission only when results would change management. To achieve clinical remission in luminal IBD, infliximab and adalimumab trough concentrations in the range of 3-8 and 5-12 μg/mL, respectively, were deemed appropriate. The range may differ for different disease phenotypes or treatment endpoints-such as fistulising disease or to achieve mucosal healing. In treatment failure, TDM may identify mechanisms to guide subsequent decision-making. In stable clinical response, TDM-guided dosing may avoid future relapse. Data indicate drug-tolerant anti-drug antibody assays do not offer an advantage over drug-sensitive assays. Further data are required prior to recommending TDM for non-anti-TNF biological agents. CONCLUSION: Consensus statements support the role of TDM in optimising anti-TNF agents to treat IBD, especially in situations of treatment failure.
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 | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".