Tumour necrosis factor inhibitors in inflammatory bowel disease: the story continues
Bibliographic record
Abstract
In the 1990s, tumour necrosis factor-α inhibitor therapy ushered in the biologic therapy era for inflammatory bowel disease, leading to marked improvements in treatment options and patient outcomes. There are currently four tumour necrosis factor-α inhibitors approved as treatments for ulcerative colitis and/or Crohn's disease: infliximab, adalimumab, golimumab and certolizumab pegol. Despite the clear benefits of tumour necrosis factor-α inhibitors, a subset of patients with inflammatory bowel disease either do not respond, experience a loss of response after initial clinical improvement or report intolerance to anti-tumour necrosis factor-α therapy. Optimizing outcomes of these agents may be achieved through earlier intervention, the use of therapeutic drug monitoring and thoughtful switching within class. To complement these approaches, evolving predictive biomarkers may help inform and optimize clinical decision making by identifying patients who might potentially benefit from an alternative treatment strategy. This review will focus on the current use of tumour necrosis factor-α inhibitors in inflammatory bowel disease and the application of personalized medicine to improve future outcomes for all patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".